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Location: New York City, NY; San Francisco, CA; Seattle, WA About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role At Anthropic, we're building AI systems that are safe, beneficial, and transformative. Our mission is to develop AI that benefits humanity, and we believe the most powerful capabilities emerge when we thoughtfully bridge the gap between research breakthroughs and real-world applications. Claude is getting good at research. Given a well-posed problem, it can read the literature, write and run simulations, analyze results, and iterate on a design across physics, materials, chemistry, and engineering. But most of the hardest problems in science and engineering aren't well-posed. They live inside companies and labs with their own data, tools, constraints, and experts, and progress depends on turning all of that into work a model can actually do and check. Our Applied AI, Science & Engineering team takes Claude to the scientists and engineers working on hard problems in energy, materials, hardware design, and other physical-world fields. We build the tools  Claude needs to do real research and engineering work. Mostly that means software: the agent harnesses, integrations, evaluations, and working processes that turn a partner's problem into something Claude can make progress on and validate. We find real-world problems and test what we build through partner engagements, then feed our learnings back to our research and product teams. It's early, and the engineers who join now will shape how Claude gets applied to science and engineering. This is an engineering role first. You'll spend real time with domain experts, become the person who knows how to get Claude working in their field, and prototype quickly alongside them. Most of your time, though, goes into building and hardening the systems that make that work repeatable, not into writing recommendations or configuring someone else's product. A science or engineering background is a big plus, but what matters most is that you can earn the trust of expert researchers, turn a fuzzy problem into something concrete and checkable, and ship. Responsibilities Build the agent harnesses and research loops that let Claude carry a problem from literature review through hypothesis generation, simulation, analysis, and design iteration Connect scientific computing and simulation tools (finite element, CFD, electromagnetic, or molecular modeling codes, for example) so Claude can drive them reliably and at scale Design and build evaluations that tell us whether Claude's work is actually right, and be honest about where it isn't Sit with scientists and engineers at partner organizations to learn their problem, data, tools, and constraints, and turn open-ended questions into well-specified, verifiable tasks with clear success criteria Prototype with partners, ship pilots into their workflows, and cut anything that doesn't move the problem forward Turn what works in one engagement into shared tools, reusable components, and documented processes the next one can start from Be the technical voice on how Claude performs on science and engineering work, explaining results, limits, and tradeoffs clearly to researchers, engineering leads, and non-technical stakeholders alike Partner with our research and product teams to share where models fall short on science and engineering work, and help shape what comes next You may be a good fit if you Have 8+ years of experience building software, with strong engineering fundamentals and comfort across the stack, from data pipelines to tooling to quick interfaces Have built real systems with large language models, including agents, tool use, or evaluations Have worked on technical problems in a science or engineering field, such as physics, materials, chemistry, or electrical or mechanical engineering Have a track record of zero-to-one work in startup or startup-like environments\ Can work directly with expert users, understand their workflows deeply, and still keep the focus on building the right system rather than the one first requested Have good judgment about what can and can't be verified, and are comfortable saying a result isn't good enough yet Bring high agency, pick up new domains quickly, and hold strong opinions loosely Communicate clearly with researchers, engineers, and external partners, and care about the societal impacts of your work Strong candidates may also have An advanced degree or research experience in a physical science or engineering field Hands-on experience with scientific computing and simulation, numerical methods, or optimization Experience in industrial R&D, a national lab, or a deep-tech company in areas like energy, materials, manufacturing, or hardware Experience building alongside customers or technical partners, for example in forward-deployed, applied AI, or solutions engineering roles, ideally where you also owned the software that came out of it Experience with ML research, RL environments, or evaluation design Candidates need not have 100% of the skills listed above Formal certifications or education credentials Expertise in every scientific domain we work in Direct machine learning or AI research experience Deadline to apply: None. Applications will be reviewed on a rolling basis. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $405,000 — $485,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: San Francisco, CA | New York City, NY | Seattle, WA About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Frontier AI is on track to be among the most consequential and most adversarially-targeted technology in the world. The capability curve is steep, the adversaries who want these systems are extremely well-resourced, and the security bar this will eventually require is well beyond where the industry operates today. Incremental hardening alone is not going to close that gap, so we need breakthroughs and a group of people to go find them. Security Labs is that team. We run a portfolio of high-risk, high-expected-value security projects: the work that seems impractical until someone optimistic and stubborn enough actually tries it. Projects run on the order of weeks rather than quarters, and each one is either handed off to the Anthropic team that will own it in production or wound down with a writeup of what we learned. We expect a meaningful fraction of our bets not to land. This team is intentionally scrappy and ambitious. We're intentionally iterating fast on high-risk research bets and we expect a meaningful fraction not to land. If a 30% project success rate sounds uncomfortable, or uncharted and chaotic territory isn’t both frightening and exciting, this probably isn't the right fit. There are other places in Anthropic Security doing important work with more structure, less risk, and more certain paths to positive outcomes. The questions we're trying to answer include: Can our core research workflows survive extreme isolation? Can we replace trust with cryptographic guarantees? Can the models themselves become our most effective security control? What would it actually take to defend against a tier-1 state adversary, and how much of that can we build now? Who we're looking for. We're hiring generalists with rare depth. You're a strong software engineer as a baseline, and on top of that you've gone deep in at least one area most engineers don't get near: firmware or hardware security, applied cryptography, OS / kernel / hypervisor internals, formal methods, reverse engineering, or high-assurance and cross-domain systems. You've built things under your own direction, you're comfortable jumping layers when the problem demands it, and you'd rather take a swing at something that might not work than ship the safe incremental thing. You think the trajectory of AI matters a great deal, you're not comfortable with how the security side of it is going by default, and you'd rather be on the inside building the answer than watching from outside. Current Project Areas The portfolio changes as we learn. The kinds of bets currently in flight or queued: Standing up a prototype high-assurance research cluster: running real Anthropic training and research workloads under extreme isolation and physical security controls, and finding out exactly where productivity breaks and what we'd need to invent to get it back Provable inference: cryptographic verification (zero-knowledge proofs, attestation chains) that a given output came from a specific model running specific code, replacing "trust us" with math Build hypervisor-isolated microVM workload isolation across the fleet Compiling an ML kernel through a formally verified pipeline where every lowering step carries a machine-checked proof of equivalence, making compilation-layer sabotage mathematically detectable Regenerating clusters: automation that spins up a purpose-built cell, runs a workload, and tears the whole thing down on a TTL measured in hours, so attacker persistence has an expiry date Using Claude itself to drive security work end to end: threat modeling new compute platforms, rewriting critical services to zero external dependencies, running the test equipment that validates what hardware datasheets claim Part of your job is deciding what comes next. We hire people we trust to pick good bets, and project selection is owned by the engineers doing the work. Key responsibilities Own Security Labs projects end to end. You'll scope the bet, build the prototype, run it against real workloads, and bring it to either a hand-off or a documented exit Stand up novel security infrastructure fast (isolated clusters, attestation chains, hypervisor and runtime work, verification tooling) optimizing for what we learn rather than for permanence Find the receiving team early, bring them along while you build, and hand them something they actually want to own Work embedded with research and infrastructure teams (Pretraining, RL, Inference, Compute) to test whether their workflows survive what you're proposing, and document precisely where they don't Turn experimental results into short writeups that shape Anthropic's long-term security architecture, and into costed contingency plans we could execute on short notice Help pick the next round of bets and influence the industry to get better along the way Minimum qualifications Genuinely care about where AI is heading and want to work on the security problems that determine whether it goes well. This is the most important thing on this list Have real depth in at least one area most software engineers don't touch (e.g. firmware or hardware security, applied cryptography, OS / kernel / hypervisor internals, formal methods and verification, reverse engineering and exploit development, or high-assurance / cross-domain systems) Have built and shipped things under your own direction. Maybe you founded a company or research group, maintained an open-source project other people depend on, or shipped research that changed how people in your field work. We weight this far more than where you've worked or for how long Have a track record of choosing the problem yourself and seeing it through, rather than only executing a plan someone else handed you Are comfortable jumping between domains and layers of the stack when the problem calls for it, and have actually done so Have run prototypes or experiments where the goal was answering a hard question rather than shipping a permanent system, including ones that didn't pan out Write clearly enough to turn weeks of work into a couple of pages someone can act on Change your mind when the evidence says to, and are fine being the least-expert person in a room full of specialists Care about defense. Plenty of folks here come from offense and that background is valuable, but what you actually want to spend your time on now is making systems hold up Are a strong programmer (Python plus at least one of Rust, Go, or C/C++) and can stand up real infrastructure without that being the interesting part of your week Preferred qualifications Experience inside airgapped or high-side environments (classified networks, cross-domain solutions, ICS/SCADA, financial trading infrastructure) and the operational realities of working in them A background in offensive security, red teaming, or vulnerability research, with calibrated intuitions for which threats actually matter Familiarity with ML infrastructure (training pipelines, distributed schedulers, inference serving, accelerator hardware) sufficient for grounded conversations with researchers about what their workloads actually need A history of working in environments built around rapid iteration rather than rigid change control: startups, applied research groups, independent consulting, small security shops The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000 — $485,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: San Francisco, CA | New York City, NY | Seattle, WA About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the team Secure Frameworks is the team in Anthropic's Security Engineering org that builds shared security libraries and frameworks the rest of the company builds on. We collaborate closely with teams and leaders across Anthropic to own critical security foundations, including workload identity and authorization, cryptographic frameworks, and the centralized access proxy that  secures traffic to internal applications. Our mission is to make the secure thing easy and the easy thing secure by helping Anthropic engineers get secure behaviour by default and preventing entire classes of vulnerabilities through careful design. We own what we ship, help customers adopt better security practices and at times we work embedded directly in research, infrastructure or product teams. About the role You will design, build and run security foundations used across Anthropic's fleet, from threat model through production, on-call and adoption. Much of the work is greenfield: our infrastructure is growing quickly and the timelines are compressed, so you will help define the architecture rather than inherit it. Depending on your strengths you will focus on one or two of the areas below and contribute across the rest. Build critical security foundations including cryptographic frameworks, mTLS infrastructure, secure serialization, and authorization systems, designed to prevent entire classes of vulnerabilities Partner with product, research, infrastructure, and other security teams to ensure frameworks integrate smoothly with lower-layer security controls Scope, design and build security services and libraries end-to-end, maintain them in production, and drive through ambiguous technical problems with minimal oversight Build and operate the zero-trust access gateway that fronts Anthropic's internal services Design and roll out authorization frameworks so services enforce least privilege consistently instead of each inventing its own access controls Own cryptographic frameworks and libraries Mentor engineers, contribute to hiring, and grow security engineering culture across Anthropic Minimum qualifications 8+ years of software engineering experience building security-relevant systems in production, including leading complex initiatives independently Strong programming skills in Go, Rust or Python Deep understanding of authentication and authorization systems: OAuth 2.0 / OIDC, JWTs, service-to-service auth, policy-based access control Hands-on experience with PKI, X.509, mTLS and workload identity Working knowledge of applied cryptography: choosing and composing primitives correctly, key management, and the ways libraries fail in practice Experience shipping and operating high-reliability services on Kubernetes across cloud and on-prem environments, including being on call for them A track record of bringing clarity and ownership to ambiguous technical problems and driving them to resolution across teams A strong sense of developer experience Clear communication across all levels of the organization Passion for AI safety and the role security engineering plays in building trustworthy AI systems Preferred qualifications Built security frameworks or libraries adopted across an engineering organization, and done the work to drive that adoption Built or operated a zero-trust / BeyondCorp-style access gateway or identity-aware proxy Migrated a fleet to mTLS at scale, including certificate issuance and staged enforcement Maintained or contributed to open-source cryptographic libraries Designed authorization systems using policy languages or relationship-based models Familiarity with ML infrastructure (training, inference serving) The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $32,000 — $485,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: San Francisco Workplace: OnSite ABOUT US: AI NEEDS A NEW INFRASTRUCTURE LAYER. WE'RE BUILDING IT AT MODAL. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We've passed $1B in annualized run rate, up 15x from a year ago. In October 2026 we raised a $750M Series D led by Accel at a $15.75B valuation. Our team includes creators of popular open-source projects (e.g.,Seaborn https://github.com/mwaskom/seaborn,Luigi https://github.com/spotify/luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. THE ROLE: Modal’s Inference Runtime team owns the container runtime stack used to run inference and training workloads across our fleet. We work at the boundary of Linux, containers, filesystems, storage, GPU drivers, and distributed systems. Our goal is to make demanding ML workloads start quickly, run efficiently, and remain securely isolated, whether they use a single GPU, hundreds of gigabytes of memory, or multiple GPUs connected with RDMA. We’re looking for a systems engineer who enjoys working deep in the stack. You’ll build production runtime infrastructure in Rust and Go, diagnose difficult Linux and performance problems, and help determine the architecture of Modal’s container platform. You’ll also work closely with maintainers of gVisor and contribute to the runtime itself when the right fix belongs upstream. WHAT YOU'LL WORK ON: - Make container startup, checkpoint, and restore dramatically faster for large inference and training workloads - Build multi-GPU and accelerator-aware snapshotting, including efficient handling of GPU memory and RDMA-enabled workloads - Design zero-copy and low-copy data paths between container memory, filesystems, storage, and Modal’s runtime - Optimize large snapshot pipelines using techniques such as parallel uploads, direct I/O, incremental snapshots, and more efficient memory handling - Improve container image and filesystem performance across EROFS, FUSE, page caches, overlay filesystems, and remote storage - Extend our sandboxed runtime to support new GPUs, drivers, profiling tools, and device capabilities across NVIDIA and AMD hardware - Debug complex failures involving system calls, virtual memory, process lifecycle, kernel behavior, GPU drivers, and container isolation - Safely roll out runtime and kernel changes across a heterogeneous fleet using compatibility controls, scheduling constraints, feature flags, and observability - Work across the runtime, scheduler, storage, and GPU infrastructure, and take ambiguous production problems from investigation through deployment YOU MAY BE A FIT IF: - You have strong Linux systems knowledge, particularly processes, virtual memory, filesystems, system calls, scheduling, namespaces, cgroups, and signals - You’ve built or debugged container runtimes, sandboxes, the Linux kernel, or similarly low-level infrastructure - You have strong programming ability in Rust, Go, or another systems language, and an interest in becoming productive in both Rust and Go - You’ve profiled and improved systems where memory movement, I/O, synchronization, or kernel interactions dominate performance - You’re comfortable debugging across abstraction boundaries, from application behavior down through runtimes, drivers, and the kernel - You can turn loosely defined production problems into well-designed, reliable systems - You want to own important infrastructure and work closely with both internal teams and customers NICE-TO-HAVE: Any of the following would be helpful, but none is required: - gVisor, runsc, runc, OCI runtimes, seccomp, or checkpoint/restore systems - Linux kernel development, virtualization, sandboxing, kernel modules, or device proxying - CUDA, ROCm, GPU drivers, accelerator virtualization, or GPU profiling - RDMA and high-performance networking - FUSE, EROFS, direct I/O, mmap, page-cache behavior, or storage engines - Large-memory or multi-GPU inference and training systems - Contributions to open-source systems software Prior gVisor or machine-learning experience is not required. We care more about strong systems fundamentals, curiosity, and the ability to learn unfamiliar parts of the stack. WHY THIS ROLE: The container runtime is directly on the critical path for inference performance. Improvements here can substantially reduce cold starts, increase token throughput, unlock new accelerator types, and make previously impractical workloads possible. This is an opportunity to work on unusually deep systems problems with immediate production impact. You’ll have room to shape the architecture, contribute to open-source runtime technology, and take ownership of foundational infrastructure used by every inference and training workload on Modal.

Location: Remote - US Who we are At Twilio, we’re shaping the future of communications, all from the comfort of our homes. We deliver innovative solutions to hundreds of thousands of businesses and empower millions of developers worldwide to craft personalized customer experiences. Our dedication to remote-first work , and strong culture of connection and global inclusion means that no matter your location, you’re part of a vibrant team with diverse experiences making a global impact each day. As we continue to revolutionize how the world interacts, we’re acquiring new skills and experiences that make work feel truly rewarding. Your career at Twilio is in your hands. . Hiring and how we work We use Artificial Intelligence (AI) to help make our hiring process efficient. That said, every hiring decision is made by real Twilions! Also, while we are a remote-first company, you may be asked to report in person on an ad-hoc basis for team gatherings, functional off-sites or customer meetings. . See yourself at Twilio Join the team as a Staff Mobile Software Engineer for Stytch, where you'll shape the next generation of identity and fraud prevention products. About the job As a Staff Mobile Software Engineer you will help scale Stytch’s core identity and fraud products as we bring Stytch capabilities to Twilio’s full customer base, delivering secure, developer-first identity and fraud prevention experiences. You’ll build mobile SDK products that power authentication, fraud detection and prevention as well as emerging agentic identity use cases. You’ll partner closely with product and engineering leadership to design and deliver high-impact customer-facing features, and evolve our platform for Twilio’s scale. This is a rare chance to operate like a startup within Twilio: you’ll work in a high-ownership environment with startup speed, backed by Twilio’s reach and scale. Responsibilities Build and own the mobile SDKs for Stytch’s identity and fraud detection platforms on Twilio—go deep on device internals for complex signal collection to improve identification of bad actors and ship new SDK surfaces powering core authentication flows, including agenting and non-human identity use cases. Partner with Product and Engineering leadership to set direction, translate customer needs into product capabilities, and deliver high-impact roadmap features. Collaborate across Twilio and Stytch teams to align on architecture, integrate platform capabilities, and unblock cross-team initiatives. Operate with deep technical ownership: author design docs, drive key technical decisions, review code, and stay close to the systems you ship. Build in ambiguity—break down complex problems, make pragmatic tradeoffs, and design developer experiences that delight our customers. Improve production quality and resilience through strong observability, incident response and remediation. Mentor and support other engineers through pairing, feedback, and knowledge-sharing, helping raise the team’s technical bar and culture. Qualifications Twilio values diverse experiences from all kinds of industries, and we encourage everyone who meets the required qualifications to apply. If your career is just starting or hasn't followed a traditional path, don't let that stop you from considering Twilio. We are always looking for people who will bring something new to the table! Required 6+ years of professional mobile engineering experience, with deep experience in designing, building and maintaining mobile applications. Android development experience, fluent in Kotlin or Java. Expertise in mobile development ecosystems, frameworks, 3rd party libraries. Deep understanding of software engineering best practices, including monitoring, observability, and building maintainable systems. Experience making high-impact technical decisions and leading technical initiatives. A proven track record of driving technical excellence while keeping customer needs at the center of your work. Deep understanding of software engineering best practices, including monitoring, observability, and building for the long term. Exceptional communication and collaboration skills, with the ability to influence and align stakeholders. Bachelor’s degree in Computer Science or related industry experience. Excellent written and verbal communication skills. Schedule: ability to work non-standard, on-call rotation weekend and holiday hours. Desired iOS development experience. Familiarity with Kotlin Multiplatform, C/C++, Swift or Go. Experience building developer tools or SDKs. Builder at heart. Through a hobby or your profession, you are passionate about being hands on and seeing your work come to life. Location This role will be remote in either the US or Canada. Travel We prioritize connection and opportunities to build relationships with our customers and each other. For this role, you may be required to travel occasionally to participate in project or team in-person meetings. What We Offer Working at Twilio offers many benefits, including competitive pay, generous time off, ample parental and wellness leave, healthcare, a retirement savings program, and much more. Offerings vary by location. Compensation The estimated pay ranges for this role are as follows: Based in Colorado, Hawaii, Illinois, Maryland, Massachusetts, Minnesota, Vermont or Washington D.C. : $171,120 - $213,900 Based in New York, New Jersey, Washington State, or California (outside of the San Francisco Bay area): $181,200 - $226,500 Based in the San Francisco Bay area, California: $201,280 - $251,600 This role may be eligible to participate in Twilio’s equity plan and corporate bonus plan. All roles are generally eligible for the following benefits: health care insurance, 401(k) retirement account, paid sick time, paid personal time off, paid parental leave. The successful candidate’s starting salary will be determined based on permissible, non-discriminatory factors such as skills, experience, and geographic location. Twilio thinks big. Do you? We like to solve problems, take initiative, pitch in when needed, and are always up for trying new things. That's why we seek out colleagues who embody our values — something we call Twilio Magic . Additionally, we empower employees to build positive change in their communities by supporting their volunteering and donation efforts. So, if you're ready to unleash your full potential, do your best work, and be the best version of yourself, apply now! If this role isn't what you're looking for, please consider other open positions. . Stay alert to recruitment fraud We care about your safety. Scammers sometimes impersonate Twilio recruiters through fake job postings, emails, websites, or messages. Please ensure you are engaging with an official @ twilio.com email address. We will never ask for payment, gift cards, cryptocurrency, or banking information during the recruiting process. We do not make job offers without a formal interview process or conduct interviews exclusively through text-based messaging apps. . Twilio is proud to be an equal opportunity employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, political views or activity, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Qualified applicants with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Additionally, Twilio participates in the E-Verify program in certain locations, as required by law.

Location: San Francisco; New York City Workplace: Hybrid Perplexity is AI for people who expect more. On the data team, that means building the systems that make our data reliable, understandable, and usable by both humans and AI. We're looking for an analytics engineer or data engineer who wants to build the foundation for an AI-native data organization. You'll design core data models, pipelines, semantic layers, data quality systems, governance practices, and warehouse workflows that power the entire company: helping teams make strategic decisions, operate the business, and move faster with trusted data. You'll also make sure those systems are secure, privacy-aware, and legible to AI agents, data scientists, and the rest of the company. This role is for someone who can operate at the boundary of analytics engineering, data engineering, data governance, and internal product. You care about dimensional modeling, dbt standards, cost-aware warehouse design, access controls, privacy, and the details that make data trustworthy. You also believe AI should make the data stack faster, easier to maintain, and more accessible across the company without weakening security or governance. WHAT YOU'LL DO - Build the core data foundation - design and maintain high-quality data models, marts, and pipelines that make analysis fast, reliable, and reusable. - Manage the data warehouse - help own warehouse architecture, environments, permissions, performance, cost, data lifecycle, and operational hygiene so the platform scales cleanly. - Make the warehouse AI-readable - own the documentation, semantic context, metadata, lineage, and retrieval patterns that AI systems depend on to understand and query Perplexity's data correctly. - Own data modeling standards - define and champion dbt patterns, dimensional modeling practices, naming conventions, tests, and review processes. - Lead data governance practices - define standards for access, ownership, lineage, documentation, retention, quality, and sensitive data handling across the analytical warehouse. - Build with security and privacy in mind - partner with engineering, security, legal, and finance where needed to ensure data access, sharing, and AI-enabled workflows are appropriate and controlled. - Automate data quality and maintenance - build AI-assisted workflows that detect issues, explain root causes, suggest fixes, generate tests, and reduce manual firefighting. - Improve data team productivity - automate repetitive workflows, improve tooling, streamline development, and make it easier for data scientists and stakeholders to answer questions. - Partner across the company - work closely with data scientists, engineering, product, finance, and GTM teams to translate analytical needs into durable data systems. - Shape tooling decisions - evaluate build-versus-buy tradeoffs, manage vendor relationships when needed, and choose tools that scale with the team. WHAT WE'RE LOOKING FOR - 6+ years of experience as an analytics engineer, data engineer, data scientist, or closely related role. - Deep SQL expertise - you can reason about correctness, performance, joins, grain, and edge cases in complex warehouse queries. - Strong data modeling experience - you've worked hands-on with dbt (or a similar transformation framework) in production, and you understand dimensional modeling, data contracts, testing, and how analytical schemas should evolve. - Pipeline ownership - you've built, maintained, debugged, and improved production data pipelines. - Warehouse management experience - you've worked with warehouse administration, access patterns, permissions, performance tuning, cost management, or operational ownership. - Governance mindset - you think clearly about data ownership, access controls, privacy, retention, lineage, auditability, and the risks of making data too easy to access. - AI-native working style - you already use AI to speed up development, documentation, QA, exploration, and repetitive workflow automation. - Stakeholder fluency - you know how to turn messy analytical requirements into trusted models, metrics, and reusable data assets. - Autonomy and execution - you can take projects from ambiguous problem to production-quality system with minimal oversight. - Operational judgment - you care about reliability, governance, security, cost, and long-term maintainability. BONUS - Snowflake administration, optimization, cost management, or warehouse performance tuning. - Experience with RBAC, PII handling, data classification, retention policies, audit workflows, or privacy/security reviews. - Experience with Databricks or other modern data infrastructure. - Experience building semantic layers, metrics layers, metadata systems, or data catalogs. - Python experience for data tooling, automation, orchestration, or quality checks. - Previous experience as an early analytics engineer or data engineer at a high-growth startup. WHY THIS ROLE - Own the foundation - your work will determine how fast and confidently the company can use data. - Build for humans and AI - the next generation of data infrastructure needs to be understandable by agents as well as analysts. - High leverage - better models, pipelines, and tools multiply every data scientist and every stakeholder who depends on data. - Small team, broad scope - you'll have room to define standards, choose tools, and ship systems that become company-wide defaults.

Location: New York City; San Francisco About the Role We’re looking for a Senior QA Web Engineer to join our Web QA team and help maintain the quality of the core web experience used by millions of people every day. In this role, you’ll test the web products and work closely with engineers to find, report, and help resolve issues early. You’ll also look at how the web experience fits into the wider Perplexity product ecosystem, making sure key flows work consistently across surfaces. Key Responsibilities - Test Perplexity’s web product across browsers, devices, and screen sizes, checking functionality, reliability, and performance throughout the development cycle. - Run end-to-end integration testing with other Perplexity products, validating shared flows, accounts, and features. - Work directly with engineers to prioritize critical bugs, write clear and actionable bug reports, and help get issues resolved. - Create and maintain test documentation, including test cases, checklists, and test plans, keeping them current as the product changes. - Analyze high-impact user-reported issues and incidents, then share insights that help improve both the product and our QA process. - Fix small, low-risk issues yourself where appropriate, shipping simple fixes to reduce engineering overhead and speed up resolution. Qualifications - 5+ years of experience in QA, with strong hands-on experience testing web applications - Solid experience with web testing, including cross-browser and responsive testing, debugging with browser DevTools (console, network, performance), and validating functionality across environments and builds - Experience with integration and end-to-end testing across multiple connected products or services - Strong communication skills, with the ability to work effectively with engineering, product, and other QA teams - Proactive use of AI coding tools (e.g. Cursor, Claude Code) to navigate the codebase, debug issues, and ship small fixes independently

Location: Espoo Workplace: Hybrid ROLE HIGHLIGHTS: - Staff Mechanical Design Engineer - Location: Espoo, Finland - Department: Spacecraft Hardware - Reports to: Mechanical Design Engineering Team Lead - Employment type: Permanent - Workplace model: Hybrid / Office - Employment is subject to applicable security screening (incl. SUPO) WHY THIS ROLE MATTERS: At ICEYE, every satellite is a marriage of precision engineering and robust reliability. As we continue expanding both our operations and spacecraft platforms with more and more demanding mission requirements there is a need for an engineering professional who has extensive experience from mechanical engineering, system integration as well as manufacturing and qualification of space hardware. As a Staff Mechanical Engineer, you will be responsible for ensuring our engineering solutions will meet the stringent Aerospace quality requirements in terms of structural integrity, reliability and efficient and cost effective manufacturing. You will be an important part of our Spacecraft engineering community and help ICEYE continue building the world’s leading SAR satellite constellation. WHO WE ARE ICEYE is the world leader in sovereign intelligence from space. We deliver persistent monitoring capabilities to detect and respond to changes in any location on Earth. ICEYE owns the world's largest and most advanced SAR (synthetic aperture radar) satellite constellation. To our customers we provide intelligence with unmatched quality, latency and revisit times, in any weather, day or night. To governments who choose to operate their own constellation we provide this proven capability as a sovereign system. ICEYE-built constellations serve customers in defence and intelligence, environmental monitoring, insurance and emergency management. We enable fast decisions that contribute to a safer future. Founded and headquartered in Finland, ICEYE operates globally with over 1000 employees across Europe, North America, the Middle East, and Asia-Pacific. YOUR DAY-TO-DAY RESPONSIBILITIES - Lead and contribute to trade studies, design reviews (PDR/CDR), and verification and production qualification documentation (PRR/ERR). - Ensure smooth, seamless and on time product delivery, from concept to qualification. - Contribute to the continuous improvements of our current products. - Collaborate with suppliers, supply chain, AIT engineering and QA department to ensure our products are compliant with launch and on-orbit environments. - Collaborate with system and subsystem engineers to define requirements for new products. - Collaborate with both internal and external engineering teams and AIT teams during product integration and build to validate the engineered solutions are meeting the set requirements and targets. Support issue resolution if and where necessary. - When needed, engage in broader spacecraft mechanical design tasks, providing cross-disciplinary support. WHAT WE’RE LOOKING FOR Must haves: - Master’s degree in Mechanical Engineering, Aerospace Engineering, or related field - Experience in aerospace, automotive, or related industries - Experience in designing satellites structures, subsystems or similar complex electromechanical systems - Understanding of mechanical stress, vibration, and thermal effects on space structures & mechanisms - Ability to work cross-functionally with systems, electrical, and manufacturing teams - Familiarity with industry standards such as ECSS, NASA, MIL - Strong team working, analytical, documentation, and communication skills - Ability to make multi team and organisational impact Nice to haves: - Experience with PLM/PDM systems for configuration control - Hands-on experience with mechanical systems assembly, integration, and testing - Prior experience with launch vehicle or spacecraft environments (SpaceX, Rocket Lab, ESA, etc.) APPLICATION PROCESS - Talent Attraction Introduction - Hiring Manager Alignment - Task Assignment - Technical Interview - Director Interview WORKING AT ICEYE At ICEYE, you’ll join a diverse and highly engaged team united by the ambition to make the impossible possible. As a global scale-up, we combine speed and ambition with the opportunity to take real ownership from day one. Your growth, wellbeing, and success are a priority, with continuous professional development, training opportunities, and a culture where collaboration is how we win. HOW WE WORK (OUR VALUES) Make the impossible possible: We set ambitious goals and stay calm under pressure. We bring grit, optimism, and ownership when things get hard, and we keep moving until we find a way. - Be curious: Go deep, ask questions, listen carefully, and think critically. Understand the “why” behind decisions. - See the big picture: Stay close to what’s happening across the company so you can make better decisions. Consider how your work affects others. - Drive effective teamwork: Create psychological safety, invite different perspectives, and build inclusive teams. There are no bad questions. - Act as one team: We win together. We match tasks to the right owner and stay agile as priorities shift. - Have fun: What we do matters—and it should be enjoyable. Celebrate progress, take pride in results, and share the wins. BENEFITS Our benefits are designed to support your health and wellbeing, at work and beyond. We keep improving them based on employee feedback, and offerings vary by location. Talent Acquisition will confirm what applies for this role and location during the process. OUR COMMITMENT TO DIVERSITY, EQUITY, AND INCLUSION We want ICEYE to be a place where people can be themselves and do great work. Different backgrounds and perspectives make us stronger, which is why we work to create an environment where people feel included, respected, and able to speak up. Whatever your background, we want you to bring your authentic self to the table. We’re committed to fair, inclusive hiring and equal opportunity. Everyone is welcome to apply. If you need any adjustments or support during the recruitment process, tell us—we’ll do our best to help. Apply now to start your ICEYE journey, and help us continue to make the impossible possible together. Read more about ICEYE and working with us at iceye.com http://iceye.com.

Location: San Francisco, California Workplace: Hybrid ABOUT SENTRY Software runs the world and the pace is faster than ever. Sentry helps developers fix errors and performance issues before users notice, so teams can spend less time firefighting and more time building. Trusted by 200,000+ organizations, Sentry is today’s application monitoring standard and our team is building its AI-native future. ABOUT THE ROLE You’ll own AI-assisted coding within Sentry’s Platform Engineering organization, building the systems and workflows AI coding agents need to contribute reliably to our codebase. Your work will span engineering teams, with Developer Infrastructure as your home base. Your first focus will be making internal developer systems accessible to agents. Many workflows currently depend on human interaction through a UI. You’ll identify the most valuable gaps, expose those capabilities through APIs, and connect tools such as Claude Code to complete development workflows. From there, you’ll improve the quality of AI-generated pull requests and automate recurring engineering work, including dependency upgrades, minor bug fixes, and maintenance. You’ll build the context and execution environment agents need: relevant repository information, tools and permissions, state management, tests, logs, recovery mechanisms, and evaluations. You’ll partner with engineers across Sentry to understand where agents succeed, where they fail, and which changes improve the development process. This role combines technical direction with hands-on implementation, from choosing the problems to solve through building, measuring, and improving the solutions. This role is based in San Francisco, with three days per week in the office to work closely with Platform teams and engineering leadership. WHAT YOU’LL DO - Set priorities for AI-assisted development at Sentry, using engineer feedback and workflow data to identify where agents would provide the most value. - Make internal developer systems accessible through APIs and build integrations enabling agents to complete workflows across those systems. - Build context systems and agent execution tooling, including tool access, permissions, checkpoints, retries, and logging. - Develop automated checks, evaluations, and feedback loops to improve the correctness, maintainability, and review readiness of AI-generated pull requests. - Automate recurring work such as security dependency upgrades, minor bug fixes, and routine maintenance. - Improve supporting developer workflows, including issue triage and CI/CD, where they limit agent effectiveness or create repetitive work for engineers. - Lead changes across engineering teams, build agreement on priorities, and measure whether the tools reduce manual work and improve delivery. HOW YOU WORK - You’ve used AI coding agents on real development work and have informed views on their strengths, limitations, and failure modes. - You bring structure to ambiguous problems, choose a practical starting point, and follow through on delivery. - You move comfortably between infrastructure, backend systems, and developer tooling. - You know when to build, integrate an existing tool, involve another team, or stop work with limited value. - You combine data with direct feedback from engineers to assess whether a solution improves their work. - You explain technical tradeoffs clearly and build alignment across teams and senior leadership. QUALIFICATIONS - 10+ years of software engineering experience, including hands-on ownership of production systems. - Experience building developer tools, infrastructure, or automation used by engineering teams, such as CI/CD systems, internal APIs, code review tooling, or agent workflows. - Hands-on experience using AI coding tools and agents, with an understanding of how context, tool access, and validation affect their output. Prior machine learning experience is not required. - Strong software engineering and system design fundamentals, including experience building reliable integrations across systems. - A track record of leading cross-team technical initiatives from problem definition through implementation and adoption. - Clear written and verbal communication, including explaining technical decisions and recommendations to senior engineering leaders. NICE TO HAVE - Experience building agent harnesses, context systems, evaluations, or automated pull request workflows. - Experience with code review tooling, static analysis, or automated testing. - Experience measuring and improving developer experience or engineering productivity. - Experience with large-scale distributed systems or monolith decomposition. - Public contributions to developer tooling or AI-assisted development projects. The base salary range that Sentry reasonably expects to pay for this position is $220,000 to $400,000 USD, plus an annual performance bonus of up to 15% of base salary. A successful candidate’s actual base salary (or hourly wage) amount will be determined by a variety of relevant factors including, without limitation, the candidate’s work location, education, work and other relevant experience, skills, and job-related knowledge. A successful candidate will be eligible to participate in Sentry’s employee benefit plans/programs applicable to the candidate’s position (including incentive compensation, equity grants, paid time off, and group health insurance coverage). See Sentry Benefits https://sentry.io/careers/ for more details about the Company’s benefit plans/programs. EQUAL OPPORTUNITY AT SENTRY Sentry is committed to providing equal employment opportunities to its employees and candidates for employment regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, or other legally-protected characteristic. This commitment includes the provision of reasonable accommodations to employees and candidates for employment with physical or mental disabilities who require such accommodations in order to (a) perform the essential functions of their jobs, or (b) seek employment with Sentry. We strive to build a diverse team, with an inclusive culture where every teammate can thrive. Sentry is an open-source company because we believe that everyone, everywhere, should have the ability and tools to make great software. Software should be accessible. That starts with making our industry accessible. If you need assistance or an accommodation due to a disability, you may contact us at accommodations@sentry.io. Want to learn more about how Sentry handles applicant data? Get the details in our Applicant Privacy Policy https://sentry.io/careers/applicantprivacy/.

Location: London Workplace: Remote WHO ARE WE? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! WHY THE COHERE TPM ROLE? As our Generative Machine Learning organization has continued growing, we are looking for more Technical Program Managers to join the team! At Cohere, TPMs are pivotal to our success, being seen as operational experts specializing in cross-functional efforts. Great TPMs at Cohere are well-rounded, executing with precision at the tactical level, while partnering with senior leaders and shaping the big picture at a strategic level, and the opportunities are endless in growing the scale and impact of their work. Cohere’s TPMs would often be described as organized, execution-oriented, pragmatic leaders who are adaptable to the needs of the company and the programs they lead. As a reward for the depth and breadth of expertize that you bring to the table, you will get to work alongside some of the most talented AI researchers and industry experts in the world on building cutting edge AI technology. No day will be the same as the one before, and the dynamic high growth environment will be absolutely perfect for anyone excited about solving novel problems, bringing structure to oftentimes chaotic and fast-paced environments, and ambitious about scaling their own impact and growth alongside the company! Please note: While we are remote-first and accept candidates from all over the world, we are highly prioritizing hiring for people who are within 1-2 hours of British Standard Time (BST). AS A MACHINE LEARNING TECHNICAL PROGRAM MANAGER, YOU WILL: - Establish, improve, and scale foundational processes that allow the Generative Modeling teams to execute better, faster, and more predictably - Scale the operational capacity and effectiveness of the Modeling TPM team in collaboration with the Modeling TPM lead to ensure the function can remain lean and effective during a period of the program portfolio complexity and size continuing to rapidly increase - Flexibly adapt to the needs of the company, programs, and the team from tackling long-horizon programs that can span quarters or even years to jumping onto often highly ambiguous short-term challenges that can have company-wide impact - Partner directly with leaders within and outside of the Modeling organization on efforts to design, train and improve our core generative models - Lead cross-functional, oftentimes company-wide programs and strategic initiatives, collaborating and coordinating with cross-functional teams such as Product, Engineering, Legal, Platform, Infra, Policy, and Finance - Operate as an organizational expert in improving engineering execution rigor and operations on the ground, while contributing to the strategic layer in areas such as long-term strategic planning with senior modeling leadership - Map out dependencies and risks, and ensure that cross-functional execution across the program runs smoothly, communicating status with non-technical and technical stakeholders and senior management - Both own and enable a useful communication flow of priorities, ideas, and knowledge throughout and beyond the Modeling organization in line with your programs - Respond quickly to program-level changes, clarifying new scope, and managing resource needs in response to evolving priorities YOU MAY BE A GOOD FIT IF YOU HAVE: - 5-10 years as a Technical Program Manager AND 5+ years of experience working directly on AI/ML. Strong preference for candidates that have worked directly in building foundation models. - In-depth knowledge of machine learning, particularly when it comes to Natural Language Processing & Large Language Models (LLMs). Bonus points for experience with Multimodal AI. - Experience with working in a chaotic, fast paced, low structure environment. We need someone who is pragmatic, can roll up their sleeves when needed, and can function at the tactical and strategic level to do whatever it takes to make Cohere’s models succeed - Personal hands-on understanding of the unique challenges of managing projects and programs in the AI/ML development space as opposed to traditional software program management - Managed projects & programs requiring a strong general business acumen, such as coordinating with Finance on reporting needs or Policy on regulatory compliance matters — the broader your experience beyond ML technical program management, the better! - (optional, but preferred) Have technical experience in a hands-on capacity. We don’t necessarily need someone with hands on experience training models, but you need to feel comfortable sitting in the same rooms with some of the best researchers in the world, working on highly complex, cutting edge ML problems If some of the above - other than the ML TPM experience - doesn’t line up perfectly with your experience, we still encourage you to apply! If you consider yourself a thoughtful worker, a lifelong learner, and a kind and playful team member, Cohere is the place for you. We welcome applicants of all kinds and are committed to providing both an equal opportunity process and work environment. We value and celebrate diversity and strive to create an inclusive work environment for all. FULL-TIME EMPLOYEES AT COHERE ENJOY THESE PERKS: - A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch. - Full health and dental benefits, including a separate budget for mental health. - RRSP matching, 401K, Pension Scheme. - 100% Parental Leave top-up for up to 6 months, for either parent. - Annual enrichment benefits: Arts & culture, fitness/wellness, quality time, and a workspace improvement credit. Education & learning stipend for conferences, courses, and coaching. - 6 weeks of paid vacation (30 working days!) - Budget for traveling to other offices if you are remote, plus an annual company offsite. HOW AND WHERE WE WORK: - Cohere is remote-friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening soon. - For those in the office: a daily lunch program, plenty of snacks, and regular community and social events. - For those not near an office: a co-working benefit so you can work alongside others in your city. - Everyone receives a $500 home office stipend to set up your workspace properly. If any of the above doesn’t line up exactly with your experience, we still encourage you to apply. We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form https://docs.google.com/forms/d/12a6IrLdF3kI2nonKSr4tiFuz18rLQbaeYV-JM9L4o9Q/edit, and we will work together to meet your needs. We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider. Beware of Scams: Cohere will never ask for payment or third-party services (e.g., CV writing) as part of our hiring process. All legitimate roles are listed on the Cohere careers page and LinkedIn only, with all communications from Cohere employees coming from an @cohere.com or @cw.cohere email alias. If jobs are viewed on other sites then please verify these through our official careers https://cohere.com/careers page.

Location: San Francisco, CA Workplace: Hybrid About Niantic Spatial AI has learned from almost everything online, but it still struggles with the physical world, where 99.5% of economic activity happens. It doesn’t know where things are, how spaces fit together, or what changes over time. That’s one of the biggest unsolved problems in AI, and it’s the one we’re solving at Niantic Spatial. We build spatial AI models and services so people and machines can reason, learn and act safely in the physical world. We turn camera imagery, video, sensor data and archives into accurate, AI-readable representations of real places that robots can train in and machines can use to know exactly where they are. In physical AI, mistakes have real consequences. That’s why we build AI grounded in measured reality, not guesswork, so the people and machines relying on it can trust it to get things right. Our team built some of the world’s most widely used maps. Now we’re building something bigger: a living, measured record of the real world that machines can query, learn from and use to understand how places change over time. If you want to help AI understand the world we actually live in, come build with us. About the Role Niantic Spatial makes the physical world computable, helping people and machines collaborate safely by aligning how they understand reality. One of the fundamental problems we're helping autonomy teams and engineers overcome is the sim-to-real gap for visual-spatial understanding. We're applying our team's decades of experience encoding the world precisely as it is at Google Maps, Google Earth, and Niantic Labs to build the scalable real-to-sim stack for embodied AI. We're looking for a Staff Engineer, Robot Learning to lead engineering for our real-to-sim product within the Embodied AI group. One of the fundamental problems facing autonomy teams is the sim-to-real gap in visual-spatial understanding, and we're applying decades of experience encoding the world precisely as it is to build the scalable real-to-sim stack for embodied AI. Working alongside the group's General Manager and Product leader, you'll inform the roadmap, own the technical requirements our researchers and engineers build against, and personally prototype and evaluate the changes you propose. You've lived the problems our customers are solving: you've trained and deployed embodied AI systems, seen where and why simulation fails to predict real-world performance, and worked through the consequences. This is not a role for someone who wants to manage and not ship, or someone who wants to ship without ownership of commercial consequences. This is a role for an entrepreneurial tech lead who loves to build and is energized by helping those around them do the best work of their lives. You have a co-founder mentality. You may have been part of a founding team before, or want to start your own business one day. What You'll Do - Sharpen the Roadmap - Bring first-hand knowledge of customer pain, existing solutions, and the value of doing better to roadmap decisions, and help focus effort on the most consequential problems to solve. - Own the Technical Requirements - Guide the team to design the real-to-sim stack pragmatically. Make the final recommendation on what to build, integrate, and simplify, ensuring capabilities work within customers' existing training and evaluation workflows. - Benchmark the Impact - Working with our Real-World Test Lab, design and run reproducible evaluations that measure the impact of our real-to-sim stack on policy transfer and real-world performance against customers' existing approaches. - Build and Ship - Lead engineering delivery and personally build critical features. Keep scope tight, remove blockers, and get performant capabilities into customers' hands quickly. - Raise the Technical Bar - Set a high bar for the team's work and deepen its understanding of robotics development and deployment, driving incremental improvement every week. What You'll Bring - Experience training, evaluating, and deploying robot policies in systems used by enterprise or government customers, with responsibility for performance in real operating conditions. - Deep practical experience using simulation for policy training or evaluation, including tools such as NVIDIA Isaac Sim, Isaac Lab, MuJoCo, or equivalent. You have diagnosed sim-to-real failures and understand when greater fidelity improves outcomes. - Hands-on experience with reinforcement learning, imitation learning, or both, and with deep-learning frameworks such as PyTorch. - A practical understanding of how visual perception and 3D scene representations affect robotics training, evaluation, and real-world performance. - Experience running experiments on real robots, from an ambiguous hypothesis through to defensible results. - Strong Python and C++ skills, with experience building reliable production software. - A bachelor's degree or higher in a relevant field. Nice to Have - You've authored or maintained a public robotics benchmark. - You've helped take an early product to repeatable adoption across multiple customers. - You've built or substantially extended robotics simulation infrastructure, including scene authoring, domain randomization, or large-scale training and evaluation. - You've trained and deployed vision-based navigation or mobility policies, particularly in varied, unstructured environments. - You've built pipelines that turn real-world reconstructions into simulation-ready environments. Competencies - Always Learning. Staying on top of the latest academic breakthroughs and commercial advancements, particularly in real-to-sim-to-real. - Relentless bias for action. You make decisions and ship as if the company’s success depends on it, because it does. You set the pace, and drive the people around you to rise to it. - Intellectually honest. You know there’s just one job that underpins every other in a startup: find the truth. You are relentless in your pursuit of it, including when it challenges your own convictions, and you raise concerns when you see them — even when it's uncomfortable, and especially when it's unpopular. - Pragmatic. You have no patience for not-invented-here syndrome and analysis paralysis. You find the fastest path to delivering customer value without going through one-way doors that undermine future scaling. - Confident and credible with customers. You can sit alongside an autonomy engineer and help them get to the root of their problems. You are comfortable speaking to customers and bring commercial nous to those discussions. Compensation & Benefits The expected salary range for this role is $270,000 – $300,000 per year. Compensation also includes an annual bonus, equity, and a comprehensive benefits package including medical, dental, and vision coverage, 401(k), and more. Location & Work Model This role is based in our San Francisco office, with three days per week in office. Inclusive Application We know the strongest candidates don't always tick every box. If you're excited about this role and believe you could do it well, we encourage you to apply even if your experience doesn't match every qualification listed - you may be exactly who we're looking for. Equal Opportunity Niantic Spatial is an equal opportunity employer. Individuals seeking employment at Niantic Spatial are considered without regard to race, color, ancestry, national origin, religion, creed, age, gender (including pregnancy, childbirth, breastfeeding or related medical conditions), marital status, physical or mental disability, medical condition, genetic information, military or veteran status, gender identity, gender expression, sexual orientation, or any other protected category under applicable laws. Niantic Spatial will also consider qualified applicants with criminal histories in accordance with applicable laws. Please contact your recruiter if you want to request an accommodation for the job application or interview process. Candidate Privacy I understand that by submitting my job application, the information I provide as part of that application will be used in accordance with Niantic Spatial's Privacy Notice for Job Applicants and Candidates https://www.nianticspatial.com/applicant-privacy-notice.

Location: San Francisco, CA | New York City, NY | Seattle, WA About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role: Large teams of agents are starting to solve problems no single agent can, from rewriting major codebases to formalizing landmark mathematics. Our team studies how these teams scale: what happens to performance, cost and coordination as the number of agents, the compute budget and the length of the task grow, and what has to change to keep getting returns from that scale. We build the platform and evaluations Anthropic uses to run and measure large agent teams, and other research teams build on them. This role lives at the boundary between research and engineering. It is a generalist role on a small team: you'll design and run large experiments, build the systems they run on, and get to the bottom of surprising results. We often need to go from a vague question to a running experiment quickly. Responsibilities: Design, run and interpret large-scale experiments on agent teams, reasoning rigorously about what the data does and doesn't show Investigate how performance and efficiency change as team size, compute and task horizon grow, and find the bottlenecks that limit them Build and scale the systems that run very large agent teams reliably, and debug the failures that only appear at scale Design evaluations for long-horizon problems, and keep their results trustworthy Build the tooling and metrics that let researchers see what a large agent team is doing and why Partner with research teams across Anthropic so they can run their own experiments on the platform, and communicate findings clearly You may be a good fit if you: Have significant software engineering, ML or research engineering experience Have owned something substantial end to end, such as a large system, an evaluation or benchmark, an agent product, or a research project Genuinely enjoy both research and engineering work Think quantitatively about complex systems, and think twice before trusting a number Can work from a vague question rather than a spec Are results-oriented, with a bias towards flexibility and impact Have clear written and verbal communication Care about the societal impacts of your work Strong candidates may also have: Experience building or operating large-scale distributed systems, such as schedulers, sandboxed code execution, or inference and RL infrastructure Built evaluations, benchmarks or harnesses for LLMs or agents Experience building complex agentic systems that use LLMs Experience with scaling laws or other large-scale empirical research A background in operations research, statistics, economics, physics, quantitative finance, or another field that models and optimizes complex systems Strong candidates need not have: Formal certifications or education credentials Academic research experience or publication history Prior experience with multi-agent systems or reinforcement learning Representative projects: Measure how performance scales with the number of agents on a hard problem, and explain where the curve bends and why Prepare our largest-ever agent run: find what breaks as team size and task length grow together, and fix it before launch Work out how to allocate a fixed compute budget across a team of agents to solve a problem fastest Build tooling that turns the activity of a large agent team into something a researcher can read in minutes Design a novel eval that distinguishes real gains in teamwork from artifacts of the evaluation setup The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $500,000 — $850,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: Canada What The Role Is We're hiring a Staff MLE for the Discovery team to own recommendations and personalization across Babylist's consumer experience — the homepage feed, product recs, search, and the ML-powered systems that make registry building feel effortless. Babylist was built on editorial recommendations: products chosen by people with deep baby gear expertise. That editorial foundation is a big part of why millions of families trust us. We're now building ML-powered personalization on top of it, using one of the richest first-party datasets in parenting. We're early in this work, and we have a real mandate. A small Discovery team has initial retrieval and reranking models live on parts of the site and a steady cadence of A/B tests. We are looking for a Staff MLE who has seen personalization done well at scale and can set the technical direction for where we go next. Registry building is the heart of the Babylist product. Every parent builds a list of dozens of products, from swaddle to stroller, with real stakes (a friend or family member is going to buy these things, and a baby is going to use them). As a universal registry, this registry also typically spans many retailers. This makes registry building on Babylist one of the most interesting personalization problems in consumer e-commerce: latent intent, life-stage progression, multi-stakeholder gift dynamics, deep declarative signal in millions of completed registries, cross-retailer datapoints, and a user who genuinely wants help. If you want to join a mature ML org and tune models at the margins, this isn't the right role. If you've worked inside a strong recommendations team, learned what good looks like, and want to build from zero-to-one at a company earlier in the journey, read on. What You'll Own You'll be the technical lead for personalization on the Discovery team, working alongside a PM, Engineering Manager, Senior MLE and fullstack software engineers. You set where our models go over the next year or two, sequence the bets that get there, and stay deep in building. A few examples of the problems you’ll get to shape: Product recommendations across every surface. Product detail pages, add-next recommendations after someone adds to their registry, vibes/aesthetic representations of products and the shared models underneath them. Personalizing the Feed. Deciding which products, editorial content and social content each family sees, and in what order. Personalizing search. Bringing what we know about a family into search results, and helping decide how much search should share retrieval and ranking with recommendations. Making Addie (our AI registry assistant) better at recs. Working with the team behind Addie so its product suggestions draw on the same models and signals as the rest of the site. In practice, you will: Take a fuzzy business problem from first sketch to production model, and own whether it moved the metric. Make the modeling and architecture calls that span surfaces and are expensive to reverse. Design the evaluation, offline and online, that tells us a model is good before and after it ships. Know when a rule or a simple baseline is the right first step, and when it's time to pursue more advanced approaches. Partner with product, design and data to help shape what's worth building. Coach engineers on the team, and more broadly be an ML expert and resource to the wider engineering org. Who You Are You’ve shipped recommendation and/or personalization systems that reached real users at scale within a consumer product, and can point to the business impact of your work. You’ve done this within a team that did ML well — you know what good looks like, and are motivated to bring that to a company earlier in their ML journey. You bring: Expertise in recommender fundamentals: for example, candidate generation vs. ranking, offline vs. online evaluation, cold start solutions, and handling position and popularity bias. Fluency in the Python ML stack and comfort owning models through deployment and monitoring, including contributions to our application backend. An ability to take an ambiguous problem and start moving before anyone hands you the full picture. A builder's instinct for early-stage ML. You understand effort vs. effectiveness tradeoffs and can advocate for when a rule beats a model, or a model is “good enough” for early learnings. An outcome and user focused mindset. You measure yourself by business impact, and you can connect a model change to user experience & registry adds, conversion or revenue. AI-native daily practice. You're already using AI to move faster and improve your output, and you stay curious about what's coming next. Compensation We post real numbers. For a Canada-based Staff Engineer, the starting base salary range is 299,300 to 372,600 CAD plus a target annual bonus of 20 percent of base. That's total target cash of roughly 359,160 to 447,120 CAD. On top of that you get meaningful equity and a 401(k) match. Where you start in that range depends on your experience, and your pay grows from there with performance and scope. How We Build AI is the default here. Engineers run agentic sessions for most of the work, and a lot of the interesting engineering now lives in the scaffolding that makes the agents good: the eval harnesses, the curated context, custom review skills and fast CI. Agents also triage incidents and handle a big share of support. A human always owns the outcome. The architecture is intentionally simple: one Rails monolith, MySQL and few moving parts. That's deliberate. Simple infrastructure lets us move fast and lets AI reason about the whole system, so the hardest problems are the ones in front of customers. The Stack Core app: Rails, Packwerk, React, TypeScript, Sidekiq Mobile: iOS (Swift), Android (Kotlin) Data, search & events: MySQL, Snowflake, dbt, Airflow, Weaviate, WarpStream, Hex, Sigma Machine learning: deep learning, matrix factorization, retrieval & ranking, AWS SageMaker, MLflow AI & dev tooling: Claude Code, Devin, Linear, CodeRabbit, Warden, LangChain, Bedrock AgentCore, Maxim Infra & ops: AWS, Kubernetes, Terraform, GitHub Actions, Datadog, incident.io, LaunchDarkly Key integrations: Shopify (payments), Iterable (CRM) Why Babylist An engineer, expecting her first baby, couldn't find the registry she wanted. So she built it. That's how Babylist started, and it's still how we work: engineers solving problems for families. Becoming a parent is one of the biggest moments in a person's life. Millions reach it for the first time every year, making thousands of decisions and figuring it out as they go. That's who we build for, and we're a long way from done. Ten million people give gifts through Babylist every year. We did more than $750M in revenue in 2025, up 45 percent over the year before, and we've been profitable for eight years while staying independent. So you can take on a hard, multi-year problem without watching over your shoulder for the next round or the next correction. And the team is small, around 65 engineers, so what you ship stays visible and your scope stays wide. How We Work Remote-first across the US and Canada, and we have been for years. That's not changing. We trust you to own your time and your outcomes, and we get everyone in a room together twice a year. Teams are small, pods of three to five engineers, so nothing you ship disappears into a committee. You'll work shoulder to shoulder with product, design and data, and with the partners across the business who rely on what you ship. You'll also stay close to customers yourself: sitting in on user interviews, watching session recordings, riding along with support. Here that's part of the engineering job, on a regular basis. How We Hire Three rounds, usually two to three weeks start to finish. Recruiter conversation (30 minutes). Trade context: what you want next, what we're building and straight answers on comp, team and remote. Technical screen (1 hour). One round, no AI, language-agnostic. Reach for Google when you normally would. We just want to see how you reason from first principles. Final round (4 hours). Four one-hour interviews: system design, AI-assisted coding with the tools you'd actually use here, product sense and culture and values. You'll hear back either way within 24 to 48 hours. If your timeline is tight, tell us and we'll move faster. Benefits Company-paid medical and fully covered dental and vision A 401(k) match Generous paid parental leave for birthing and non-birthing parents, plus a gradual return-to-work program Winter Wonder Week, a paid company-wide week off at the end of the year A remote-work stipend Mental-health and wellness support A Few Things To Know We record and transcribe interviews to evaluate candidates, in line with applicable privacy laws. We expect you to use AI in your work and we welcome it in the process, but what you submit and say should reflect your own thinking. If you have a family member or close relationship with a Babylist employee, let your recruiter know. Official outreach only ever comes from an @babylist.com address

Location: United States What The Role Is We're hiring a Staff MLE for the Discovery team to own recommendations and personalization across Babylist's consumer experience — the homepage feed, product recs, search, and the ML-powered systems that make registry building feel effortless. Babylist was built on editorial recommendations: products chosen by people with deep baby gear expertise. That editorial foundation is a big part of why millions of families trust us. We're now building ML-powered personalization on top of it, using one of the richest first-party datasets in parenting. We're early in this work, and we have a real mandate. A small Discovery team has initial retrieval and reranking models live on parts of the site and a steady cadence of A/B tests. We are looking for a Staff MLE who has seen personalization done well at scale and can set the technical direction for where we go next. Registry building is the heart of the Babylist product. Every parent builds a list of dozens of products, from swaddle to stroller, with real stakes (a friend or family member is going to buy these things, and a baby is going to use them). As a universal registry, this registry also typically spans many retailers. This makes registry building on Babylist one of the most interesting personalization problems in consumer e-commerce: latent intent, life-stage progression, multi-stakeholder gift dynamics, deep declarative signal in millions of completed registries, cross-retailer datapoints, and a user who genuinely wants help. If you want to join a mature ML org and tune models at the margins, this isn't the right role. If you've worked inside a strong recommendations team, learned what good looks like, and want to build from zero-to-one at a company earlier in the journey, read on. What You'll Own You'll be the technical lead for personalization on the Discovery team, working alongside a PM, Engineering Manager, Senior MLE and fullstack software engineers. You set where our models go over the next year or two, sequence the bets that get there, and stay deep in building. A few examples of the problems you’ll get to shape: Product recommendations across every surface. Product detail pages, add-next recommendations after someone adds to their registry, vibes/aesthetic representations of products and the shared models underneath them. Personalizing the Feed. Deciding which products, editorial content and social content each family sees, and in what order. Personalizing search. Bringing what we know about a family into search results, and helping decide how much search should share retrieval and ranking with recommendations. Making Addie (our AI registry assistant) better at recs. Working with the team behind Addie so its product suggestions draw on the same models and signals as the rest of the site. In practice, you will: Take a fuzzy business problem from first sketch to production model, and own whether it moved the metric. Make the modeling and architecture calls that span surfaces and are expensive to reverse. Design the evaluation, offline and online, that tells us a model is good before and after it ships. Know when a rule or a simple baseline is the right first step, and when it's time to pursue more advanced approaches. Partner with product, design and data to help shape what's worth building. Coach engineers on the team, and more broadly be an ML expert and resource to the wider engineering org. Who You Are You’ve shipped recommendation and/or personalization systems that reached real users at scale within a consumer product, and can point to the business impact of your work. You’ve done this within a team that did ML well — you know what good looks like, and are motivated to bring that to a company earlier in their ML journey. You bring: Expertise in recommender fundamentals: for example, candidate generation vs. ranking, offline vs. online evaluation, cold start solutions, and handling position and popularity bias. Fluency in the Python ML stack and comfort owning models through deployment and monitoring, including contributions to our application backend. An ability to take an ambiguous problem and start moving before anyone hands you the full picture. A builder's instinct for early-stage ML. You understand effort vs. effectiveness tradeoffs and can advocate for when a rule beats a model, or a model is “good enough” for early learnings. An outcome and user focused mindset. You measure yourself by business impact, and you can connect a model change to user experience & registry adds, conversion or revenue. AI-native daily practice. You're already using AI to move faster and improve your output, and you stay curious about what's coming next. Compensation We post real numbers. For a US-based Staff Engineer, the starting base salary range is $233,500 to $290,700, plus a target annual bonus of 20 percent of base. That's total target cash of roughly $280,200 to $348,840. On top of that you get meaningful equity and a 401(k) match. Where you start in that range depends on your experience, and your pay grows from there with performance and scope. How We Build AI is the default here. Engineers run agentic sessions for most of the work, and a lot of the interesting engineering now lives in the scaffolding that makes the agents good: the eval harnesses, the curated context, custom review skills and fast CI. Agents also triage incidents and handle a big share of support. A human always owns the outcome. The architecture is intentionally simple: one Rails monolith, MySQL and few moving parts. That's deliberate. Simple infrastructure lets us move fast and lets AI reason about the whole system, so the hardest problems are the ones in front of customers. The Stack Core app: Rails, Packwerk, React, TypeScript, Sidekiq Mobile: iOS (Swift), Android (Kotlin) Data, search & events: MySQL, Snowflake, dbt, Airflow, Weaviate, WarpStream, Hex, Sigma Machine learning: deep learning, matrix factorization, retrieval & ranking, AWS SageMaker, MLflow AI & dev tooling: Claude Code, Devin, Linear, CodeRabbit, Warden, LangChain, Bedrock AgentCore, Maxim Infra & ops: AWS, Kubernetes, Terraform, GitHub Actions, Datadog, incident.io, LaunchDarkly Key integrations: Shopify (payments), Iterable (CRM) Why Babylist An engineer, expecting her first baby, couldn't find the registry she wanted. So she built it. That's how Babylist started, and it's still how we work: engineers solving problems for families. Becoming a parent is one of the biggest moments in a person's life. Millions reach it for the first time every year, making thousands of decisions and figuring it out as they go. That's who we build for, and we're a long way from done. Ten million people give gifts through Babylist every year. We did more than $750M in revenue in 2025, up 45 percent over the year before, and we've been profitable for eight years while staying independent. So you can take on a hard, multi-year problem without watching over your shoulder for the next round or the next correction. And the team is small, around 65 engineers, so what you ship stays visible and your scope stays wide. How We Work Remote-first across the US and Canada, and we have been for years. That's not changing. We trust you to own your time and your outcomes, and we get everyone in a room together twice a year. Teams are small, pods of three to five engineers, so nothing you ship disappears into a committee. You'll work shoulder to shoulder with product, design and data, and with the partners across the business who rely on what you ship. You'll also stay close to customers yourself: sitting in on user interviews, watching session recordings, riding along with support. Here that's part of the engineering job, on a regular basis. How We Hire Three rounds, usually two to three weeks start to finish. Recruiter conversation (30 minutes). Trade context: what you want next, what we're building and straight answers on comp, team and remote. Technical screen (1 hour). One round, no AI, language-agnostic. Reach for Google when you normally would. We just want to see how you reason from first principles. Final round (4 hours). Four one-hour interviews: system design, AI-assisted coding with the tools you'd actually use here, product sense and culture and values. You'll hear back either way within 24 to 48 hours. If your timeline is tight, tell us and we'll move faster. Benefits Company-paid medical and fully covered dental and vision A 401(k) match Generous paid parental leave for birthing and non-birthing parents, plus a gradual return-to-work program Winter Wonder Week, a paid company-wide week off at the end of the year A remote-work stipend Mental-health and wellness support A Few Things To Know We record and transcribe interviews to evaluate candidates, in line with applicable privacy laws. We expect you to use AI in your work and we welcome it in the process, but what you submit and say should reflect your own thinking. If you have a family member or close relationship with a Babylist employee, let your recruiter know. Official outreach only ever comes from an @babylist.com address

Location: Remote, Canada; Remote, United States GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. * Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. An overview of this role Data Products integrates GitLab and third-party SDLC data, and builds the dashboards, APIs, and data products that turn it into trusted, interoperable intelligence for customers and internal teams. This is a 0 to 1 team and we are hiring for a foundational engineering role. That means you bring technical leadership through ambiguity, helping figure out what to build, making the foundational design decisions, and keeping delivery moving while the picture is still forming. You own the full lifecycle of what ships and stand behind it. What you'll bring Proven ability to influence as a Staff owning product or platform systems end to end across multiple engineering and product teams. High agency, i.e finds the owner, clarifies the path, makes the call, and keeps pushing until the work lands. Force multiplier and raise the people and the codebase around them Deep full-stack expertise, including strong proficiency in Go, Ruby and Node . Depth in at least one of analytics and metrics platforms, graph data systems, CDC and warehouse integration. Hands-on experience building production AI-powered tool orchestration. Experience operating multi-tenant systems across SaaS and self-managed deployment. Direct communicator who builds alignment across teams. Bias to write things down and make reasoning legible. What you'll own Architect how GitLab and third-party data is ingested, modeled, and synced into the knowledge graph as a near-real-time graph of the development ecosystem. Lead integration with external systems for business context (Jira, observability tools, Zendesk, ServiceNow), indexed into the graph. Architect the Data Marketplace so customers consume GitLab data through Snowflake, Databricks, and BigQuery with no engineering dependencies. Publish GitLab observability as OpenTelemetry and broker the data-sharing APIs. Define operational readiness for everything the team ships across GitLab.com, Dedicated, and Self-Managed. Translate an ambiguous 0 to 1 problem space into concrete, iterable roadmaps in partnership with Product, Design, and the graph backend team. Resolve cross-team dependencies across graph backend, AI Platform, and Infrastructure without escalating everything upward. Mentor senior and intermediate engineers through design reviews, pairing, and code review. Create organizational leverage through speed. You identify systemic bottlenecks, simplify processes, and design systems that dramatically improve execution velocity. About the team Data Products work across three surfaces: Dashboard and Reporting Framework Builds AI-native dashboard components so customers and internal teams can see engineering health and GitLab's impact across DORA, VSA, AI Impact, SDLC trends, security, and quality, and launch their own custom dashboards on the same framework. Internal Ingestion Ingests third-party data into the GitLab and power tools like Knowledge Graph, building connectors so that signals from systems like Jira, Zendesk, ServiceNow, and observability tools become one queryable, near-real-time graph of the developer ecosystem. External Ingestion External makes GitLab and Knowledge Graph data consumable outside the product, through marketplace listings, data-sharing APIs and SDKs, and co-branded partner products. We are a new team in a highly ambiguous, high-impact space, and we need engineers who can create clarity while building, make practical tradeoffs, unblock cross-team dependencies, and keep pushing until meaningful customer value lands. The base salary range for this role’s listed level is currently for residents of the United States only. This range is intended to reflect the role's base salary rate in locations throughout the US. Grade level and salary ranges are determined through interviews and a review of education, experience, knowledge, skills, abilities of the applicant, equity with other team members, alignment with market data, and geographic location. The base salary range does not include any bonuses, equity, or benefits. See more information on our benefits and equity . Sales roles are also eligible for incentive pay targeted at up to 100% of the offered base salary. United States Salary Range $152,800 — $259,200 USD How GitLab Supports Full-Time Employees Benefits to support your health, finances, and well-being Flexible Paid Time Off Team Member Resource Groups Equity Compensation & Employee Stock Purchase Plan Growth and Development Fund Parental Leave Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application. Country Hiring Guidelines: GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process. Privacy Policy: Please review our Recruitment Privacy Policy. Your privacy is important to us. GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics. See also GitLab’s EEO Policy and EEO is the Law . If you have a disability or special need that requires accommodation , please let us know during the recruiting process .

Location: Canada Workplace: remote About Xsolla Xsolla is a global commerce company with robust tools and services to help developers solve the inherent challenges of the video game industry. From indie to AAA, companies partner with Xsolla to help them fund, distribute, market, and monetize their games. Grounded in the belief in the future of video games, Xsolla is resolute in the mission to bring opportunities together, and continually make new resources available to creators. Headquartered and incorporated in Los Angeles, California, Xsolla operates as the merchant of record and has helped over 1,500+ game developers to reach more players and grow their businesses around the world. With more paths to profits and ways to win, developers have all the things needed to enjoy the game. For more information, visit xsolla.com. About the Role We're looking for a Staff Engineer to own the smart wallet and account abstraction layer of Xsolla's Web3 platform. This is an individual contributor role with outsized technical scope: you'll be the technical anchor for how millions of players get an on-chain account they never have to think about — setting direction, making hard architectural calls, and raising the bar across the org. Concretely, you'll own the account model behind Xsolla ZK, our zkSync-based L2, and the wallet that sits inside Xsolla App. Every player who signs in with Xsolla ID receives a deterministic, non-custodial smart account — derived from their identity claim, identical on every device, with private keys that never leave the client. That account is not a separate "wallet" screen: it is the player's inventory, and it backs minting, gifting, trading on our Bazaar marketplace, Merkle-based reward airdrops, gas sponsorship, and an in-app dApp browser that exposes the same signer to third-party applications through an EIP-1193 provider. You'll own the primitives underneath all of it: the ERC-4337 user operation lifecycle, EIP-7702 delegation, session keys and origin-bound Smart Sessions, paymaster and gas abstraction strategy, and the custody and recovery roadmap — including the move from raw key export to MPC custody. You will own the strategy behind account abstraction, signing, and session management at scale, and evolve our smart account and permissioning model to meet both product and compliance needs. You operate with significant autonomy, but your decisions ripple across teams — so you'll spend real time building buy-in with engineering, product, security, and legal stakeholders, not just designing in isolation. You are technically deep, calm under pressure, and comfortable being the escalation point when production wallet issues get hard — because in this domain, "hard" means a stuck user operation, a drained paymaster, or a signing prompt a player didn't understand. You write the RFCs and design docs that people actually read, and you create leverage for the broader engineering org through documentation, tooling, and mentorship — without needing a management title to do it. Responsibilities Own Smart Wallet & AA Architecture — Own the technical strategy and architecture of our smart wallet platform, covering the account model, signing, session management, and gas abstraction at scale. Design the Account & Signing Model — Design and evolve our ERC-4337 user operation lifecycle (prepare → sign → submit → confirm), EIP-7702 delegation, deterministic address derivation from identity, and the session-key and permission primitives that let players act without a prompt on every transaction. Own Gas Abstraction — Set the paymaster and gas-sponsorship strategy: what we sponsor, how we meter and cap it, how we defend it from abuse, and how it degrades when sponsorship is unavailable. Harden the dApp-Facing Surface — Own the security model where our signer meets third-party applications: EIP-1193 / EIP-6963 provider behavior, origin-bound and policy-versioned sessions, trust tiers, and the transaction and signature confirmation flow. Define what a player is actually approving, and make sure the UI can prove it. Drive Custody & Recovery — Lead the custody roadmap from today's interim mechanisms toward MPC-based custody, and design recovery paths (passkeys, guardians, social recovery) that a non-crypto-native player can complete without losing assets. Drive Cross-Team Technical Decisions — Drive decisions on account architecture, SDK contracts, data modeling, and platform reliability, and build buy-in across the wallet, marketplace, SDK, mobile, and security teams. De-Risk Proactively — Identify systemic risks — key management, signature phishing, reorgs and indexer staleness, bundler and RPC dependencies, contract upgrade paths — and lead initiatives to resolve them before they become incidents. Set Engineering Standards — Define engineering standards, review critical code, contracts, and designs, and create leverage for the team through documentation, tooling, and mentorship. Align with Stakeholders — Collaborate with product, security, legal, and infra teams to align on roadmap and translate business and regulatory needs into well-scoped technical plans. Own Production Escalations — Serve as the go-to escalation point for complex production issues in the wallet and account abstraction domain. What You Bring Account Abstraction & Smart Wallets ERC-4337 Depth — Deep, hands-on understanding of ERC-4337: EntryPoint (v0.6 through v0.8), the UserOperation lifecycle, bundler and paymaster roles, validation rules (ERC-7562), gas estimation and its failure modes, counterfactual deployment, and signature validation for undeployed accounts (ERC-6492). EIP-7702 & the Post-Pectra Landscape — Working knowledge of EOA delegation, its interaction with ERC-4337 EntryPoint v0.8, and the trade-offs and risks it introduces (residual key authority, delegation phishing, storage collisions). Smart Account Implementations — Experience building on or extending production smart account implementations (Safe, Kernel, Biconomy, Coinbase Smart Wallet, or equivalent), and familiarity with modular account standards (ERC-7579 / ERC-6900). Session Keys & Delegated Permissions — Experience designing scoped, time-limited, policy-bound authority: session keys with TTL and contract allowlists, origin binding, permission revocation, and delegated-permission standards (ERC-7710 / ERC-7715). Signature Schemes & Wallet Interfaces — Practical command of EIP-712 typed data, ERC-1271 contract signatures, WebAuthn / passkey (P-256) signers, and the wallet-facing standards: EIP-1193, EIP-6963, EIP-5792 batched calls, ERC-7677 paymaster interfaces. Production Wallet Experience — Experience designing or operating a production smart wallet or account abstraction system serving real users and real value. Production Crypto Track Record Scale in a Major Crypto Organization — Substantial engineering experience at a large crypto product or protocol organization — for example Coinbase, Kraken, Base, Safe, the Ethereum Foundation, Consensys / MetaMask — or a comparable wallet, exchange, or account-abstraction infrastructure team where wallet correctness and custody were core to the product. Operational Judgment Under Value at Risk — You have shipped and operated systems where a bug meant lost funds, and you can talk concretely about the guardrails, reviews, and rollback paths you put in place. Smart Contracts & EVM Solidity & EVM Fluency — Ability to read, review, and write account, paymaster, and module contracts; gas-aware design; upgradeability and migration patterns; working effectively with external auditors. L2 Specifics — Understanding of L2 execution differences and how they affect wallet design: native account abstraction on zkSync-family chains, transaction and fee models, finality and reorg behavior, and RPC and indexer semantics. Backend Engineering Go Engineering — Strong Go (Golang) engineering skills: idiomatic code, concurrency patterns, performance profiling. TypeScript & SDK Design — Ability to design and own client-side SDK surfaces in TypeScript that other engineering teams — internal and external — build on top of. Distributed Systems — Experience with distributed systems and their trade-offs (consistency, availability, failure modes), and with reconciling off-chain state against an authoritative on-chain result. Data & Infrastructure PostgreSQL — Schema design, query optimization, migrations at scale. Kubernetes — Deploying, operating, and debugging services in a k8s environment. Message Streaming — Kafka or NATS — event-driven patterns, consumer groups, at-least-once delivery. Git & CI/CD — Git and modern CI/CD practices. Security Key Management — Hands-on experience with key material in production: platform secure storage (Keychain / Keystore), HSMs, MPC or threshold signing, and the operational discipline around each. Wallet Threat Modeling — Solid grasp of the wallet attack surface: signature and delegation phishing, malicious dApp origins, replay and cross-chain replay, token approval abuse, and paymaster griefing and DoS. Leadership Cross-Team Initiative Leadership — Proven ability to lead multi-quarter technical initiatives across teams. Architectural Influence — Track record of influencing architecture and standards beyond your immediate team. Written & Verbal Communication — You write RFCs and design docs that people actually read. Nice to Have Experience working in the video game industry, building or operating platforms for game developers, publishers, or players Hands-on experience with the zkSync / Elastic Chain ecosystem (ZKsync OS, Matter Labs stack, native AA and paymaster flows, custom L2 operations) Experience with embedded and ecosystem wallet providers or AA infrastructure vendors (thirdweb, Privy, Dynamic, Turnkey, Pimlico, Alchemy, Biconomy, ZeroDev, Safe{Core}) — and clear views on where to build versus buy Experience with MPC / threshold custody, social recovery, or guardian-based recovery in a consumer product Experience with NFT and token infrastructure at scale: ERC-721 / ERC-1155 / ERC-20 mechanics, marketplace contracts, Merkle-based airdrop and claim distribution, chain indexing services Contributions to open-source wallet, security, or identity projects — including authorship or review of ERC / EIP standards Familiarity with compliance requirements relevant to crypto consumer products: KYC / AML, geofencing, MiCA, SOC 2, ISO 27001, GDPR data minimization, audit logging Experience with identity and auth integration for wallet derivation (OAuth 2.0 / OIDC, JWT verification, token scoping) Background in platform or infrastructure engineering — building systems other engineers build on top of Experience owning a mobile wallet surface (React Native / Expo, WebView provider injection, native signing UI) Hands-on, up-to-date experience with modern AI tools (e.g. Claude, Copilot, Cursor) for code generation, review, and accelerating day-to-day engineering work How We Work Xsolla operates across multiple time zones, and the smart wallet underpins identity, ownership, and value transfer for every Web3 product we ship. Strong written communication is essential — your architectural decisions and designs need to stand on their own and be actionable without you in the room. We value directness, technical depth, and follow-through. In this domain that means saying plainly when a design has custody, security, or reversibility implications, defending your position with evidence, and staying engaged until it's resolved. Where a decision touches player funds or key material, we expect the conservative call to be argued for explicitly rather than assumed. Benefits We are passionate about fostering a supportive environment for our team, so we prioritize the physical, mental, and emotional well-being of our employees and their families through a comprehensive Benefits Program. This includes medical, dental, and vision, PTO, and a personalized career roadmap for each employee. By investing in professional development through training and educational opportunities, we ensure that our team thrives both personally and professionally. Together, we’re not just building a business; we’re cultivating a community that values creativity, collaboration, and the transformative power of play.

Location: Richmond, Canada 📍 Location: Richmond, BC, Canada 📄 Employment Type: Full-Time 🏢 Work Arrangement: Hybrid 🚀 Start Date: TBD 💰 Compensation: TBD — depending on skills and experience Important Screening Requirements Graduate degree in Electrical Engineering, Engineering Physics, Mathematics, Physics, or a related field , or an equivalent combination of relevant education and experience. 15+ years of experience working with radar, Synthetic Aperture Radar (SAR), or similar active imaging systems. Experience spanning hardware, processors, and/or data products within radar or SAR systems. 10+ years of experience in space systems engineering or a similarly complex systems engineering environment. Experience with SAR remote sensing , including system design, operations, applications/exploitation, and/or calibration. Experience working across engineering lifecycles for large, complex programs . Must be able to obtain and maintain Reliability Status security clearance and meet Controlled Goods Program (CGP) requirements. Strong problem-solving and analytical skills. Excellent verbal and written communication skills. Ability to communicate complex technical concepts to technical teams, executives, customers, and other stakeholders. Ability to work effectively within a large organization and navigate cross-functional dependencies. About the Role We are seeking a Staff Systems Engineer – Synthetic Aperture Radar (SAR) Missions to provide end-to-end technical oversight of SAR-related engineering activities throughout the lifecycle of complex space missions. This role will contribute to the development of advanced SAR missions that generate high-value imagery and actionable intelligence for customers. You will work across mission concept development, spacecraft and payload trade studies, SAR system design, mission-level integration and verification, commissioning, calibration, and early operations . This is a highly senior technical role requiring the ability to connect mission-level requirements with spacecraft systems, SAR payloads, processing systems, and end-user applications . What We're Looking For We’re looking for a Staff-level SAR systems engineer with extensive experience bridging SAR technology and space mission systems engineering . The ideal candidate has the technical depth to work across the SAR lifecycle—from mission concept and CONOPS through payload and spacecraft trade studies, image-quality analysis, integration, verification, commissioning, calibration, and early operations . This role requires someone who can operate comfortably at both the deep technical and mission/program levels , communicate with a broad range of stakeholders, mentor engineers, and provide technical guidance on complex SAR mission challenges. Key Responsibilities Mission Concept & Systems Engineering Provide end-to-end oversight of SAR-related engineering activities across the full lifecycle of a space SAR mission. Lead development of the Mission Concept of Operations (CONOPS) . Define high-level mission specifications based on user and customer requirements. Develop and assess spacecraft-level budgets supporting SAR payload requirements. Translate mission objectives into technical requirements and system-level solutions. SAR Mission Design & Technical Solutioning Lead technical solutioning for enhancements to existing and future SAR missions. Support and lead SAR beam-mode design . Develop and assess SAR mission concepts. Support SAR image-quality modeling and performance analysis. Evaluate technical solutions across mission, spacecraft, payload, processor, and application domains . Mission-Level Trade Studies Lead design trade studies across: Spacecraft systems Coverage and access SAR payload SAR processor End-user applications Evaluate technical, operational, and mission-level dependencies. Balance competing system requirements and performance objectives. Provide technical recommendations and guidance to program leadership. Risk Management, Integration & Verification Lead mission-level technical risk management. Oversee mission-level integration activities. Lead or support verification and validation activities. Identify technical dependencies, interfaces, and potential mission-level risks. Ensure SAR mission requirements and technical objectives are addressed throughout the engineering lifecycle. Commissioning, Calibration & Operations Provide technical leadership for SAR mission commissioning and calibration. Support early operations of space-based SAR missions. Contribute to the development and execution of mission commissioning strategies. Support resolution of technical issues during early mission operations. Apply SAR system knowledge to evaluate mission and image-quality performance. Technical Leadership Provide technical guidance to senior management and new business activities. Communicate complex SAR and space systems concepts to diverse audiences. Support customer discussions and technical presentations. Mentor and coach engineering team members. Help develop technical capabilities and domain expertise across the organization. Required Qualifications Graduate degree in Electrical Engineering, Engineering Physics, Mathematics, Physics, or a related discipline , or equivalent combination of education and relevant experience. 15+ years of experience with radar, SAR, or similar active imaging systems. Experience involving one or more of: Radar/SAR hardware SAR processors SAR data products 10+ years of experience in space systems engineering or a similarly complex systems engineering environment. Experience in SAR remote sensing , including system design, operations, applications/exploitation, and/or calibration. Experience working across engineering lifecycles for large and complex programs. Strong problem-solving and analytical capabilities. Excellent verbal and written communication skills. Ability to communicate effectively with technical teams, executives, customers, sales/business teams, and other stakeholders. Ability to work effectively within a large organization and navigate cross-functional dependencies. Ability to understand and manage diverse stakeholder needs. Ability to obtain and maintain Reliability Status security clearance . Ability to meet Controlled Goods Program (CGP) requirements. Preferred Qualifications Experience with MATLAB and/or Python . Strong understanding of satellite-based SAR payload design and operations . Experience with space mission design, including: Coverage and access Orbital dynamics Mission geometry Understanding of spacecraft design considerations, including: Thermal Power Pointing accuracy Spacecraft interfaces Strong understanding of geospatial data . Experience with SAR image-quality analysis and modeling . Experience working across SAR payload, spacecraft, processor, and end-user application interfaces . Ability to rapidly learn new technologies and domain-specific concepts.

Location: Seattle, WA Workplace: Remote ABOUT DOCKER Docker has been one of the most loved brands in developer tooling, trusted by more than 20 million monthly users and over 20 billion container image pulls. From solo founders to the world's largest companies, developers rely on Docker to build, share, and run their applications across our suite of products including Docker Desktop, Docker Hub, and Docker Scout. We are a globally distributed, remote-first team building the tools that define how software gets built and delivered. As AI agents redefine software development, Docker is at the center of that shift, providing the sandboxed environments, verified images, and secure infrastructure that make autonomous workflows trustworthy by default. _______________________________________________________________________ We're building AI-native development practices into how this team works at a foundational level. That means infrastructure design needs to account for a new kind of collaborator: AI agents that generate, deploy, and operate software. The Staff Infrastructure Engineer on this team will keep systems running, define what safe, observable, AI-assisted infrastructure operations look like in practice, and set the standard for how the broader engineering organization follows. WHAT YOU'LL WORK ON The Billing Platform Engineering team owns the systems that make Docker's commercial model real. You'll work on problems like: - How do we design infrastructure that makes AI-generated deployments safe to ship and easy to roll back? - How do we instrument billing systems so that failures — billing miscalculations, entitlement gaps, payment errors — are detected immediately and unambiguously? - How do we build infrastructure that scales with usage-based billing workloads without manual intervention? - How do we make the developer experience on this team faster and more reliable — local environments, CI/CD pipelines, deployment tooling? RESPONSIBILITIES - Own and evolve the infrastructure supporting Billing Platform services: compute, storage, networking, CI/CD, and observability - Design and maintain IaC (Terraform) for billing system infrastructure on AWS; set module patterns and standards for the team - Build and own observability systems — metrics, logging, alerting — with a focus on billing accuracy and payment reliability - Define deployment patterns and runbooks that work well in an AI-agent-assisted development workflow: clear rollback procedures, safe promotion gates, automated validation - Partner with software engineers on service design — bringing infrastructure constraints and operational requirements into the conversation before code is written - Identify systemic risks and drive improvements that span team or organizational boundaries - Lead incident response for billing system issues. This role may require participation in an on-call rotation to provide support outside of standard business hours, including evenings, weekends, and holidays, as needed. - Mentor engineers across the team; your technical judgment should raise the floor for everyone QUALIFICATIONS - 8+ years in platform, infrastructure, or SRE roles supporting production SaaS systems at scale - Deep AWS expertise: ECS or EKS, RDS (Postgres preferred), networking, IAM, cost management — you've operated these systems under real load and real incidents - Expert-level Terraform; you've designed reusable module patterns and set standards others follow - Experience building and owning observability stacks (Datadog, Grafana, or similar) at an organizational level — not just using them - Strong familiarity with CI/CD systems — Jenkins, GitHub Actions, or equivalent — including pipeline design and developer experience ownership - Kubernetes at an operational and architectural level - A track record of identifying systemic risks and driving improvements that span team or organizational boundaries - Security-first mindset: threat modeling, blast radius analysis, least-privilege by default, audit trails as a design requirement - Strong written English; at Staff level, written communication is how you scale your influence across teams - Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience WHAT SETS YOU APART You don't wait for problems to be handed to you — you find them, frame them, and drive the solution. You've operated at a scope where your decisions affected multiple teams or systems, and you know how to build consensus and move work forward without direct authority. You've thought seriously about what infrastructure needs to look like when AI agents are generating and shipping code — safe deployment patterns, strong observability, clean rollback — and you want to help define that standard here. Experience with billing, payments, or financial systems infrastructure is a meaningful plus. WHAT TO EXPECT FIRST 30 DAYS You will ship code in your first week. We run an agent-first development workflow — infrastructure changes start with a plan, specifications are written before generation, and every change is reviewed before it merges — and onboarding is no exception. You will get hands-on with the infrastructure supporting Billing Platform services early, shadow on-call, and build a clear picture of the system before you start making bigger changes. By the end of 30 days you will have shipped real work and know where the most important problems are. FIRST 90 DAYS You will have taken ownership of one or more infrastructure components and delivered an improvement from design to production with measurable impact. You will be an active participant in deployment and reliability discussions, bringing infrastructure constraints and operational requirements into the conversation early — before code is written. You will be a full participant in the on-call rotation and have begun shaping the team's technical direction. ONE YEAR OUTLOOK You will be the team's trusted authority on billing infrastructure. You will have driven meaningful improvements to observability, deployment safety, or platform reliability — and your work will be directly visible in the resilience and correctness of systems that handle real financial transactions for millions of Docker users. You will have helped define what AI-agent-assisted infrastructure operations look like done right, and that standard will be visible beyond this team. Docker considers sponsorship on a case-by-case basis based on business needs.   Compensation & Equity United States: $170,350 – $275,550 + equity ______________________________________________________________________ POSTING INFORMATION - Open vacancy: This posting is for an existing open role. - AI in hiring: Docker may use AI-assisted tools during our recruiting process. - Interview recordings: Candidates will be invited to opt in to interview recordings to support interviewer calibration and consistent evaluations. Recordings are optional and require explicit consent. ______________________________________________________________________ PERKS & BENEFITS - Remote-first by design – Work from your home, with offices in Seattle and Paris for connection and collaboration. - Flexibility that fits your life – We trust you to manage your schedule while delivering great work. - Time to recharge – Generous PTO, designated quarterly Whaleness Days, and a designated end-of-year Whaleness break. - Home office support – Set up your workspace for comfort and success. - Technology stipend – Equivalent to US$100 net per month to help support your work. - Learning & development – Annual stipend for conferences, courses, certifications, and continued learning. - Parental leave – 16 weeks of paid parental leave after six months of employment. - Equity for all full-time employees – Share in Docker's long-term success as we continue to grow. - Comprehensive benefits – Medical, retirement, and paid holidays vary by country. - Docker swag – Because representing the whale never gets old. Docker is proud to be an equal opportunity employer. We are committed to building a team that reflects a broad range of backgrounds, experiences, and perspectives. We believe diverse teams build better products, make better decisions, and better serve our global community. #LI-REMOTE