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Location: San Francisco; Palo Alto; New York City ABOUT THE ROLE The Backend Platform team is the backbone of Perplexity’s product reliability, scalability, and performance, enabling every product and AI team to build with confidence. This high‑impact group builds and maintains critical infrastructure, including backend systems for authentication, authorization, real-time data flows, service orchestration, and sandboxing, to ensure fast, reliable, and secure user experiences. By setting rigorous standards for code quality, uptime and performance, the Platform team ensures everyone at Perplexity can innovate at speed on top of a robust, well-architected foundation. KEY RESPONSIBILITIES - Lead the design and implementation of our sandboxing platform to safely execute untrusted and user‑generated code while meeting strict performance and reliability requirements. - Define and extend our RBAC and authorization primitives, ensuring they scale with new product capabilities and enterprise controls. - Own key backend services involved in authentication and encryption flows (including BYOK‑related pieces), partnering with Security and Infra on defense‑in‑depth. - Set the technical bar for backend platform design and operations, driving best practices in reliability, observability, and incident response for always‑on systems. - Partner with product and AI teams to define clear platform interfaces, SLAs, and integration patterns so new features can reliably build on shared backend capabilities. - Provide technical leadership across the backend platform area, mentoring other engineers and making long‑term architectural decisions that keep the foundation simple, secure, and evolvable. QUALIFICATIONS - Experience designing and building backend systems that run in production (typically 4+ years for mid-level, more for senior and staff). - Strong system design skills, with a track record of creating efficient, reliable, and scalable architectures. - Hands-on experience with modern infrastructure (for example AWS, Kubernetes, and related cloud technologies). - Strong proficiency in at least one backend language such as Python, Go, or Rust, and the ability to work effectively in a multi-language environment. - Collaborative mindset and eagerness to solve challenging technical problems with other experienced engineers. - Comfort working in security‑sensitive areas (auth, authorization, encryption) and making pragmatic trade-offs between safety, simplicity, and velocity. If you’re excited about this role, we encourage you to apply even if your experience doesn’t match every qualification listed above.

Location: San Francisco Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW We are looking for a Staff Technical Program Manager, Platform & Infrastructure to drive Harvey's most critical platform and infrastructure programs as the business scales. You will be the connective tissue across Core Infrastructure, Backend Platform, and product engineering teams, and cross-functional teams such as Security and Finance, owning the multi-quarter horizontal initiatives that define how Harvey scales: cloud migrations, cost optimization, capacity and performance planning, BYOC, and the buildout of Harvey's internal platform foundations. You will be going deep technically with senior platform and infrastructure engineers, aligning EMs and execs around durable plans, and driving programs from ambiguous mandate through measurable delivery. WHAT YOU'LL DO - Own end-to-end execution of Harvey's most strategic platform & infrastructure programs, including cloud migrations, infrastructure cost optimization, capacity planning, BYOC, and the buildout of the new Backend Platform team's foundations, partnering deeply with Infra and Platform leadership. - Lead Harvey's infrastructure cost program: establish ownership, accountability, and reporting mechanisms across engineering teams; partner with Finance on forecasting and unit economics. - Drive horizontal platform & infrastructure initiatives end-to-end, coordinating across engineering, security, and GTM teams as needed to unblock execution and align stakeholders. - Own structured engagement with strategic external vendors (AWS, Azure, OpenAI, Anthropic), including capacity commitments, contract optimization, escalation paths, and joint planning. - Define, measure, and report on platform & infrastructure health metrics (cost, capacity headroom, migration progress, platform adoption, reliability SLOs), and hold the org accountable for sustained improvement. - Identify systemic gaps in platform maturity, infrastructure planning, capacity, and observability; convert findings into structured multi-quarter roadmaps with clear owners, milestones, and risk surfacing. - Run the operating cadence for platform & infrastructure programs: working groups, steering reviews, exec readouts, and risk reviews. Make program status legible to leadership without creating overhead for engineering teams. - Partner with senior ICs and EMs to evaluate architectural tradeoffs, sequence dependencies across teams, and unblock execution when programs hit friction. WHAT YOU HAVE - 8+ years of experience in technical program management, with a track record of driving large, ambiguous, multi-quarter platform or infrastructure programs to completion at scale. - Deep technical fluency in core platform and infrastructure domains: cloud platforms (AWS, Azure, GCP), Kubernetes, databases, networking, observability, CI/CD, and platform/service frameworks. You don't need to write production code, but you read system designs critically and push back substantively. - Experience driving at least one of: a cloud migration at scale, a major infrastructure cost program with measurable financial impact, an internal platform buildout, or a comparably large horizontal platform/infrastructure initiative. - Demonstrated ability to lead and influence senior engineering stakeholders, including Staff+ ICs, engineering managers, and executive sponsors, without direct authority. - Excellent written and verbal communication, with a particular ability to distill complex technical tradeoffs into crisp narratives for exec audiences and across functions. - A track record of building durable mechanisms (review forums, metrics, operating cadences) that outlive any individual program and scale with the org. - Comfort with ambiguity. You're energized by problems that aren't yet well-defined and where the first step is to scope the problem itself. - Bachelor's degree in Computer Science, Engineering, or related technical field. BONUS POINTS - Experience driving infrastructure cost programs in a high-growth environment (target metrics like cost per tenant, infrastructure as % of revenue). - Experience leading a cloud migration, BYOC rollout, or major platform consolidation end-to-end. - Experience standing up or scaling an internal platform / developer experience function from the ground up. - Experience managing strategic vendor relationships with hyperscalers (AWS, Azure, GCP) or foundation model providers (OpenAI, Anthropic). - Familiarity with the modern infra stack at scale: Terraform, Kubernetes, service meshes, data platforms, and observability tools (Datadog, Prometheus, Grafana). - Prior experience as an engineering manager, platform/infrastructure engineer, or SRE before moving into program management. COMPENSATION $188,000 - $278,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES [HERE https://www.notion.so/harveyai/Harvey-Candidate-Privacy-Policies-319ac3fcdd7a803bb807d5094f249922]. #LI-AK1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: New York, NY (HQ); San Francisco, CA Workplace: Hybrid ABOUT RAMP Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $275B in annualized spend flows in and out of 85,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books. The problems are high-stakes, data-dense, and unforgiving. We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome. The median Ramp customer saves 7.5% and grows revenue 16% in their first year – far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same. If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it. ABOUT THE ROLE At Ramp, people are the single biggest lever on our trajectory, and the bar for who joins this team gets higher every quarter, not lower. We're hiring a Recruiter who treats recruiting as a research discipline as much as a relationship one: someone who can map a market, identify the operators who'll outperform their peers, and run a full-cycle process that wins them. You'll own end-to-end hiring across Sales and GTM functions, from first conversation through offer close, and partner with GTM leadership to translate ambitious hiring plans into real teams. The recruiters who do this best at Ramp combine craft on the front end (research, identification, calibration) with execution on the back end (closing, candidate experience, pipeline discipline). We're looking for both. WHAT YOU'LL DO - Use AI and research to find top talent. You'll build target lists of high performers at peer and aspirational companies using LinkedIn Recruiter, Sales Navigator, Gong signals, internal data, and AI tools like Claude. We expect AI to be part of your daily workflow, not a novelty, but a core part of how you identify, qualify, and reach candidates. - Calibrate quickly and map the market. For every new search, you'll develop a clear point of view on what "great" looks like, who fits the profile, and where to find them, then share that POV with hiring managers as a written artifact rather than just a slate of resumes. - Run thoughtful, personalized outreach. The best replies come from outbound that's grounded in real research, a candidate's territory, deal motion, or recent move. We care more about reply rate and onsite conversion than send volume. - Own the full cycle, from first outreach through offer close, including pipeline hygiene in Ashby, stakeholder updates, and offer construction. - Close candidates with competing offers. Many of our strongest GTM hires are weighing multiple options, and you'll partner with hiring managers and leadership to build conviction, sequence references, and run a closing motion that wins on substance. - Be a strategic partner to GTM leaders. Push back when a search is mis-scoped, surface compensation reality early, and translate hiring-manager wishlists into a concrete profile within days. - Deliver an excellent candidate experience that's fair, transparent, and fast. Feedback is honest; offers move in days, not weeks. - Improve how we hire by contributing to process changes, comp benchmarking, employer brand, and how Ramp evaluates GTM talent. WHAT YOU NEED - 5+ years of full-cycle GTM/Sales or G&A recruiting, ideally in a high-growth or top-tier startup environment where the bar was non-negotiable. - A real track record of identifying strong talent, not just running searches that closed. Bring examples of candidates you found, ran a process for, and who became top performers. - Comfort using AI in your day-to-day workflow. You already use LLMs to research candidates, draft personalized outreach, summarize calls, or surface signals from messy data, or you're eager to. We see AI fluency as core to how recruiting works going forward. - Research instincts. You can articulate why a given candidate is in the top decile of their cohort, not just that they have the right title at the right company. - Closing skill. You've personally closed senior hires against competing offers from top-tier companies. - Data literacy. You're comfortable in Ashby (or equivalent ATS) and with funnel metrics, and willing to challenge a hiring manager with the numbers when needed. - High urgency operating mode. You book next steps on the call, not after, and you own the candidate's experience through to signature. - Excellent written communication. Your outbound and your candidate updates are the work product, and they should be specific, sharp, and not template-driven. NICE-TO-HAVE - Direct experience recruiting AEs, Sales Managers, RevOps, or Sales Leadership at a high-growth fintech, SaaS, or infra company. - Experience building or contributing to an AI-augmented research workflow. - Familiarity with Ashby, LinkedIn Recruiter, Sales Navigator, and Gong. BENEFITS AVAILABLE TO ALL FULL-TIME RAMP EMPLOYEES (GLOBAL) - Flexible PTO - Centralized home-office equipment ordering - Health and wellness stipend - Budget for intra-office travel - Weekly coffee stipend UNITED STATES - 100% medical, dental & vision insurance coverage for you, with partial coverage for dependents - One Medical annual membership - 401(k), including employer match on contributions made while employed by Ramp - Fertility HRA (up to $10,000 per year) - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay - Pet insurance - In-office perks: lunch, snacks, drinks, and more - Relocation expense coverage to NYC or SF (if needed) CANADA - Group medical, dental, and vision coverage through Sun Life - Life, AD&D, and disability coverage - Fertility drug coverage (up to $4,000 lifetime) - Group Retirement Plan with employer match (RRSP + DPSP) - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay, with additional time available at reduced pay - Employee Assistance Program and virtual care through Lumino Health UNITED KINGDOM - Private medical insurance through Freedom Elite - Virtual GP and at-home care via eMed x Livi - Workplace pension through Penfold, with salary sacrifice option - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay with additional time available at reduced pay REFERRAL INSTRUCTIONS If you are being referred for the role, please contact that person to apply on your behalf.   OTHER NOTICES Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.   Beware of recruiting scams: Ramp will only contact you through official @Ramp.com http://Ramp.com email addresses and will never ask for payment or sensitive personal information during the hiring process.   Ramp Applicant Privacy Notice https://ramp.com/legal/applicant-privacy-notice

Location: New York 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: We’re looking for a Growth Engineer to own the technical foundation of Modal’s marketing and developer-facing web surfaces: the marketing site, docs site, growth landing pages, high-profile microsites, forms, analytics instrumentation, and the integrations that help users discover, understand, and get started with Modal. This is a frontend-heavy role for someone with strong product taste, web engineering craft, and a business-owner mindset. You’ll partner with Product Engineering, Design, Data, and Growth to ship polished, measurable web experiences, from high-profile projects like the GPU Glossary https://modal.com/gpu-glossary and LLM Engine Advisor https://modal.com/llm-almanac/advisor to internal tooling that helps teams publish content faster. When this role is going well, Modal launches new pages, docs experiences, campaigns, and experiments quickly without sacrificing performance, craft, or measurement. WHAT YOU’LL WORK ON: - Own and evolve Modal’s marketing site, docs site, and technical web experiences as high-quality engineering systems, not static content - Build bespoke interactive projects with engineers, turning deep technical work into useful web experiences for developers and agents - Instrument all public-facing content, all the way from human-facing marketing pages to agent-targeted, self-improving documentation - Improve internal publishing flows so more teams can ship quality content quickly while keeping the site maintainable YOU MAY BE A FIT IF: - You’ve built and shipped modern web applications end-to-end - You have experience with agentic development flows where agents consume marketing content and docs to help developers build on Modal - You can build polished, responsive web experiences without needing every detail specified - You have a business-owner mindset and strong data intuition: you think about conversion, activation, developer trust, speed of launch, and quality of measurement, and the tradeoffs inherent to each - You’re excited about developer-facing AI infrastructure and building web experiences that feel credible to engineers - You’re able to work in person in our NYC office We care more about what you’ve built than years of experience. NICE-TO-HAVE: - Deep knowledge of SEO, AEO, structured content, or agentic discoverability - Experience with marketing sites, docs sites, or product-led-growth surfaces at a devtools, infra, or AI company - Familiarity with our stack: Svelte/SvelteKit, TypeScript, Tailwind, Vite, MDX/SVX content systems, headless CMS (Sanity), Playwright/Vitest - Familiarity with our integrations: Segment, Snowflake, PostHog, Sentry, Algolia/DocSearch, Fillout, Pylon, Ashby - Experience designing growth experiments, A/B tests, or funnel instrumentation - A portfolio of polished sites, docs systems, interactive demos, or growth experiments

Location: New York; San Francisco; Canada; United States; Toronto; Montreal 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 this role? The Data Infrastructure team at Cohere is responsible for the storage and data movement layer underlying every model training run. We're building the unified storage layer that feeds our training workloads. It needs to serve petabytes of training data and model checkpoints fast enough to keep thousands of GPUs busy across several training clusters. In this role, you’d have an opportunity to build this system from the ground up. You’d be a key contributor, working on a problem few teams have had to solve at this scale. In this role, you will: - Design, build, and operate the distributed storage system that feeds model training and evaluation. - Run this system multiple on Kubernetes clusters at petabyte scale. - Work with researchers and training-infra teams on how jobs actually read and write data, and turn that into throughput, latency, and durability requirements - Work through the networking, I/O, and consistency problems of moving large datasets and checkpoints across regions and backends, with GPU idle time and time-to-insight as the measures of success You may be a good fit if you have: - Strong storage fundamentals, including replication, consistency, caching, and data lifecycle management. - Strong coding ability. We work in Python and Go; experience in either is enough, but you should be willing to pick up the other - Experience running stateful systems on Kubernetes, including Persistent Volumes, CSI drivers, and StatefulSets. - Hands-on experience with cloud object storage such as S3 as well as POSIX-style filesystems. It’s a bonus if you have: - Experience with parallel or HPC filesystems such as Weka, VAST, or Lustre. - Familiarity with the data-loading and checkpointing patterns used in large-scale model training. - An interest in how large models are trained and evaluated, including the trajectory and eval data those runs generate, and a desire to understand the workloads the platform is serving. 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: London, UK 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 Anthropic's Infrastructure organization is foundational to our mission of developing AI systems that are reliable, interpretable, and steerable. The systems we build determine how quickly we can train new models, how reliably we can run safety experiments, and how effectively we can scale Claude to millions of users — demonstrating that safe, reliable infrastructure and frontier capabilities can go hand in hand. Cluster Infra owns the full lifecycle of compute clusters at Anthropic. We build agent-driven automation for cluster provisioning and lifecycle management across all major cloud providers and our own datacenters. Our systems stand up clusters that are interconnected with high bandwidth, secure-by-default, and able to automatically drain and recover in response to failure. As a Staff engineer on this team, you'll set the technical direction for how Anthropic brings compute online - at a moment when the scale of that compute is growing faster than at almost any company in the world. Key responsibilities Own the technical strategy and roadmap for agent-driven cluster lifecycle management - provisioning, updates and decommissioning Partner across teams to ensure new compute capacity is ingested on time Align with partner teams on physical build-out and leverage cloud solutions to deliver high-bandwidth inter-cluster connectivity Collaborate with security owners to ensure clusters are provisioned secure-by-default Define and drive strategy on cluster scalability, homogeneity and fault tolerance Work closely with cloud providers and internal research, inference and product teams to shape long-term compute, data, and infrastructure strategy Establish and evolve operational-excellence practices: incident response, postmortem culture and on-call health Support the growth of engineers around you through technical mentorship and coaching Minimum qualifications Deep expertise in distributed systems, reliability, and cloud platforms (e.g., Kubernetes, IaC, AWS/GCP/Azure) Strong proficiency in at least one systems language (e.g., Rust, Go, or Python), IaC proficiency with Terraform. Track record of leading complex, multi-quarter technical initiatives spanning multiple teams or systems Ability to build alignment across senior stakeholders and communicate effectively at all levels Preferred qualifications 10+ years of software engineering experience, including time as a technical lead setting direction for a team Experience operating large-scale compute infrastructure at hyperscale (100+ clusters, 10K+ nodes) Depth in one or more of: Kubernetes internals, cluster provisioning and management systems, cluster orchestration systems (Mesos, Borg-like) Experience with cloud networking: VPC design and peering, Shared VPC/Transit Gateway, Cloud Interconnect/Direct Connect, Cloud NAT, cross-cloud private connectivity, BGP and route control, edge load balancing and DDoS mitigation (Cloud Armor / AWS Shield) Experience with cluster and host networking: CNI (Cilium), eBPF, NetworkPolicy, multi-NIC, sFlow, service mesh (Istio/Envoy/Linkerd, mTLS) Experience with cluster security: pod security standards and admission control, RBAC and least-privilege IAM, node and container hardening, supply-chain/image provenance Deep experience with infrastructure-as-code (Terraform, Atlantis), workflow orchestration (Temporal, Argo Workflows) Skill in quickly understanding systems design tradeoffs and keeping track of rapidly evolving software systems 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: £325,000 — £485,000 GBP 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. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. 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: United States What The Role Is As a Senior Engineer at Babylist, you take on hard problems and own them end-to-end. Millions of families depend on what we build. Agents write most of the code now. So the hard part is yours: what to build, how it should work and whether what shipped was right. You're still in the code for the genuinely hard problems. Agents handle the volume. You spend your time on the parts that need a person. We're building well beyond the registry now: the financial side of raising a kid, maternal health made simpler and more human, the education new parents are looking for and the community around them. The hard problems run across all of it, plus the platform underneath and the AI we already ship to families. You'll start on a team, but you won't be boxed into it. The roadmap is open, your scope grows as you do and you have a say in what you take on next. Senior here means owning whole problems; Staff is the next step up, setting direction across a domain. What You'll Own A Senior Engineer here owns problems, not just features. You're handed an outcome, you decide what to build, ship it and own whether it worked. You act on incomplete information and change course fast when the evidence says you were wrong. What you do depends on what the team needs: Frame the problem with your PM as a peer, run the experiment and bring customer evidence into what gets built. Harden a critical path, kill a class of incidents or design the abstraction your team builds on. Be the reviewer people want on the risky changes. Manage up with crisp choices: raise the risks early and protect the right tradeoffs. Share what you're learning about AI openly, so the whole team gets sharper. A few problems people at this level are working on right now: Deciding what the registry recommends to each family, from the ranking to the model behind it, and proving in a live experiment that it actually helps. Building the evals system that catches our AI support agent making things up before a customer does. Making a child's info one source of truth instead of five disconnected flows, so parents set it up once and new products like Early Investor build on it. Who You Are You've been shipping production systems for years, and you've got the judgment to show for it. You can pick up an ambiguous problem and start moving before anyone hands you the full picture. The strongest Senior engineers here are generalists, and usually the most prolific builders on their team. You go wherever the problem leads: a backend service one week, a stubborn frontend bug the next, the data pipeline after that. When it's hard you dig in, and you learn unfamiliar parts of the stack fast. Range is the Senior signature. Compensation We post real numbers. For a US-based Senior Engineer, the starting base salary range is $202,200 to $251,700, plus a target annual bonus of 15 percent of base. That's total target cash of roughly $232,530 to $289,455. 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: San Francisco Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW As a Software Engineer, Agents, you'll build the systems that make our AI agents indispensable to legal professionals. You will design environments and actions for agentic professional work, make model selection decisions, manage context windows, create optimal tools, and develop evals that enable faster iteration loops to unlock new capabilities. We're looking for engineers and researchers who are immersed in the space, driven to ship impactful products, and are experienced in using practical evaluations to drive task completion quality and customer delight. REPRESENTATIVE PROJECTS - Save weeks of effort on high pressure M&A deals by automating information requests and diligence checks across hundreds of thousands of files with retrieval and file editing agents. - Dramatically speed up deal turnaround speed by improving the latency and quality of agents on applying a standard legal "playbook" to contracts. - Ensure that our clients win their complex, data-hungry litigation cases by optimizing our multi-source retrieval agents. - Enable board-ready PowerPoint generation by tuning the harness and libraries for coding agents. WHAT YOU'LL DO - Partner with customers and PMs to understand legal workflows, design practical evaluations that capture what “excellent” means, and ship agents that get the job done. - Optimize agent performance through prompt engineering, model selection, tool design, skill writing, context window management, and eval harness development. - Work with our model infra team to design and implement infrastructure for low-latency agent execution, including caching strategies, parallel tool calls, or subagent patterns. - Improve our observability and instrumentation to profile agent behavior, identify bottlenecks, and drive optimization decisions. - Stay current on new developments in agentic systems and bring those learnings back to the products we build. WHAT YOU HAVE - 10+ years (post-BS/MS) of software engineering experience. - Passion for building effective domain-specific agents. - Iterative mindset: you develop proof of concepts, make decisions quickly, and ship v0s. - Comfortable with when and how to use evaluations to drive quality. - Humble and adaptable about code and frameworks. We expect you to drive adoption of new best practices as they develop. - We are hiring for this role across Mid, Senior, and Staff levels. - Proficiency in Python and experience working with LLM APIs and agent frameworks. - Experience with shipping user-facing products, either on the backend or full-stack. COMPENSATION RANGE $231,000 - $340,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES [HERE https://www.notion.so/harveyai/Harvey-Candidate-Privacy-Policies-319ac3fcdd7a803bb807d5094f249922]. #LI-BB1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: Europe; United States Workplace: Remote ABOUT CANTINA Cantina is a new social platform founded by Sean Parker with the most advanced AI character creator. Our bots are lifelike, social creatures that can interact wherever people are online—across voice, video, and text. Create yourself, imagine someone new, or choose from thousands of characters to share infinitely scalable, personalized content and seamless group chat. If you’re excited about how AI can shape creativity and social interaction, come help us build what’s next. ABOUT THE ROLE: We’re looking for a Research / ML Engineer to join our Speech Team to build state-of-the-art speech systems end-to-end—from data specs through production inference. You’ll drive the model ↔ data ↔ eval flywheel for TTS and adjacent tasks (voice cloning, controllable TTS, voice conversion and more), partnering closely with research, data, and infra to ship fast, reliable, and cost-aware models. In this role, you will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems. WHAT YOU’LL DO: - Model Building: Architect, implement, pre-train, fine-tune, and post-train/alignment (e.g., GRPO/DPO) for large-scale speech models. - Project Leadership: Independently lead small research projects while collaborating on larger team initiatives. - Experimental Design: Design, run, and analyze scientific experiments to advance our understanding of the models. - Tool Development: Develop and improve dev tooling to enhance team productivity. - Full-Stack Contribution: Contribute to the entire stack, from low-level optimizations to high-level model design. - Data Ownership: Define data requirements and collaborate on acquisition, curation, augmentation, labeling quality, and synthetic data strategies. - Rigorous Evaluation: Design automated objective/subjective evaluations—listening tests, SV/WER/ASR-based metrics, robustness & bias checks, and red-team studies. - Pipeline Delivery: Harden the training → evaluation → inference pipeline; profile latency, memory, and cost; and meet production SLAs with robust monitoring and rollback. - GPU Scaling: Partner with infrastructure to run distributed training/inference on cloud fleets and productionize models with reliability and observability. - Safety & Responsibility: Contribute to safety/consent guardrails and to misuse/abuse mitigation for responsible speech technology. WHAT YOU’LL BRING: - Exceptional research/development experience with large scale audio models (>3B models and >500k hours data). - Exceptional understanding and hands-on experience with transformer architectures and/or diffusion models (inc. distillation and streaming) and/or audio language modelling. - Strong experience with multi-node and multi-gpu distributed model training. - Strong software engineering skills with a proven track record of building complex systems - Strong with PyTorch and performance work (profiling, CUDA/Triton/C++ as needed) and writing reliable production quality code. - Shipped large scale speech/audio models to production. - Background in working with large-scale ML data. - Ability to iterate on data,, and triangulate quality using subjective and objective signals. - Notable publications and/or open source contributions in speech/audio/ML. - Experience with voice-cloning, speech-control, voice-generation. PREFERRED EXPERIENCE: - Shipped large scale speech/audio models (TTS/VC/ASR) to production. - Work on large-scale ML systems. - Experience with audio language modelling, transformer architectures. - Experience with voice-cloning, speech-control, voice-generation. - Background in processing large-scale ML data. - Publications or notable open-source in speech/audio/ML. COMPENSATION: When determining compensation, a number of factors will be considered, including skills, experience, job scope, location, and competitive compensation market data.   BENEFITS FOR U.S.-BASED ROLES: - Competitive salary and generous company equity - Medical, dental, and vision insurance – 99.99% of premiums covered by Cantina - 42 days of paid time off, including: - 15 PTO days - 10 sick days - 15 company holidays - 2 floating holidays - Generous parental leave & fertility support - 401(k) retirement savings plan - Lifestyle spending account – $500/month to use however you’d like - Complimentary lunch and snacks for in-office employees - One Medical membership, and more!

Location: New York, NY (HQ); Remote (Canada); San Francisco, CA; Remote (US) Workplace: Hybrid ABOUT RAMP Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $275B in annualized spend flows in and out of 85,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books. The problems are high-stakes, data-dense, and unforgiving. We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome. The median Ramp customer saves 7.5% and grows revenue 16% in their first year – far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same. If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it. ABOUT PRODUCTION ENGINEERING Production Engineering is Ramp's infrastructure ownership layer. We exist to make Ramp faster, more reliable, and more scalable — and we do that by being embedded in the problems, not adjacent to them. A few things that define how we operate: - One team, one company, one objective. There is no "infra team" and "product team" — there is Ramp. We share the company's goals as our own. When a product team struggles with reliability or scalability, that is our struggle. - If reliability or scalability is at risk, we own it. We don't wait to be invited, and we don't ask whose code it is. If a system is slow, if it breaks, if it won't scale — that's ours to lead, regardless of where it lives in the stack. - We go first, and we go fast. When the path isn't obvious, we don't wait for someone else to find it. We move with urgency, propose the solution, align the stakeholders, and stay in until it's done — not until our ticket is closed. - We lead the way. We find the next problem before it finds us. And when we solve it, we don't just fix it for ourselves — the patterns and standards we establish become the foundation the rest of Ramp builds on. That's not a side effect of the job; it's the job. - We stay calibrated. Speed means nothing if we're moving in the wrong direction. We regularly stop and ask honestly whether what we're working on is still the highest-leverage thing we could be doing — the discipline that makes sure our effort compounds toward what actually matters. You cannot build the future of finance on shaky infrastructure. OUR TEAMS Production Engineering is organized into teams — but we think about teams differently. Teams are mutable. We build them around what needs to be done, not around what we need to do given the teams we already have. The structure below reflects our current priorities; it will evolve as Ramp does. Right now, we operate across four areas: - Compute — the foundation everything runs on: container orchestration, networking, load balancing, edge infrastructure, and the deployment systems that get code from engineers' laptops to production reliably and at scale - Storage — databases, caching, object storage, and the data infrastructure that underpins everything - Workflows & Messaging — the systems that power Ramp's financial workflows and event-driven architecture - Internal Infrastructure — the platform that makes every builder at Ramp faster and more autonomous: observability, cost attribution, and CI/CD systems that give teams visibility into what they build and what it costs WHAT YOU'LL DO Production Engineers at Ramp are full software engineers who happen to specialize in infrastructure. You write production code, you own systems end-to-end, and you drive technical outcomes across the organization — not just within your team. Day to day, you will: - Build and operate critical infrastructure across Ramp's compute, storage, messaging, and observability stack — owning the systems that handle real financial transactions at scale. - Drive architectural change — not just flag problems. When you surface a reliability or scalability issue, you own the path forward: you propose the solution, find the owners across engineering, and stay in until it's resolved. - Partner with product teams at the design phase — reviewing architectures, embedding golden paths, and making it easy to build correctly the first time. - Build Ramp's next level of scale — you'll be a hands-on contributor to the most consequential infrastructure shift happening right now: our move to a cellular architecture, enabling Ramp to scale, reach international markets, operate in highly regulated and constrained environments (e.g. FedRAMP), and deliver on enterprise-grade SLAs. - Enable AI-native engineering — as Ramp builds increasingly AI-powered products, PE is the team that makes sure the platform can support them. You'll proactively partner with product teams on AI infrastructure patterns, define the golden paths that turn one-off solutions into reusable foundations, and stay ahead of emerging challenges before they become blockers. - Build developer tooling and self-service infrastructure — so that other teams can answer their own questions (cost, performance, reliability) without involving PE. - Participate in on-call rotation — and more importantly, use every incident as a signal to eliminate the root cause, not just resolve the symptom. - Lead across the company — PE doesn't just review designs or show up when called. We proactively initiate cross-team architectural reviews, and we take full ownership of company-wide reliability and scalability initiatives: identifying the problem, proposing the solution, aligning the stakeholders, and staying in until it's done. WHAT WE LOOK FOR We don't hire for a checklist. We hire for the instincts that make someone exceptional on this team. The mindset we're looking for: - You can't walk past something broken without wanting to fix it — and you don't stop at the workaround. You find the root cause and eliminate the class of problem. - You think in systems, not tasks. You understand that "fixing the bug" is step one; making it so the bug can't happen again is the job. - You move with urgency but without chaos. You know when to go fast and when to slow down and do it right. - You treat product teams as your users. You build for them, communicate with them proactively, and measure success by how much faster they can ship. But you're also an owner — when something is broken or blocking, you don't wait to be asked. We win when our customers win. - You leave things better than you found them. Every system you touch, every process you encounter — you move it forward. Not always dramatically, but always directionally. You picked it up in a certain state; you leave it in a better one. - You are equally comfortable reading a database query plan, reviewing a system design doc, writing a Terraform module, and jumping into an incident at 2am. Experience profile: - 2+ years of software engineering experience shipping high-quality architectures for critical systems - Strong software engineering fundamentals — you write clean, well-tested, production-ready code - Hands-on experience with distributed systems at production scale - Experience with at least one major cloud provider (AWS preferred) - Familiarity with observability practices (SLOs, error budgets, alerting, dashboards) - Track record of leading technical projects end-to-end, including cross-team coordination - Comfortable using AI tooling and coding agents as part of your everyday engineering workflow — we expect our engineers to leverage these tools to move faster and think bigger Bonus (not required): - Experience with cellular or multi-tenant architecture patterns - Prior work on workflow orchestration systems (Temporal) - Contributions to developer experience or internal platform tooling - Experience in fintech, payments, or regulated industries (FedRAMP, SOC 2) BENEFITS AVAILABLE TO ALL FULL-TIME RAMP EMPLOYEES (GLOBAL) - Flexible PTO - Centralized home-office equipment ordering - Health and wellness stipend - Budget for intra-office travel - Weekly coffee stipend UNITED STATES - 100% medical, dental & vision insurance coverage for you, with partial coverage for dependents - One Medical annual membership - 401(k), including employer match on contributions made while employed by Ramp - Fertility HRA (up to $10,000 per year) - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay - Pet insurance - In-office perks: lunch, snacks, drinks, and more - Relocation expense coverage to NYC or SF (if needed) CANADA - Group medical, dental, and vision coverage through Sun Life - Life, AD&D, and disability coverage - Fertility drug coverage (up to $4,000 lifetime) - Group Retirement Plan with employer match (RRSP + DPSP) - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay, with additional time available at reduced pay - Employee Assistance Program and virtual care through Lumino Health UNITED KINGDOM - Private medical insurance through Freedom Elite - Virtual GP and at-home care via eMed x Livi - Workplace pension through Penfold, with salary sacrifice option - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay with additional time available at reduced pay REFERRAL INSTRUCTIONS If you are being referred for the role, please contact that person to apply on your behalf.   OTHER NOTICES Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.   Beware of recruiting scams: Ramp will only contact you through official @Ramp.com http://Ramp.com email addresses and will never ask for payment or sensitive personal information during the hiring process.   Ramp Applicant Privacy Notice https://ramp.com/legal/applicant-privacy-notice

Location: London, UK 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 The RL Velocity team owns the efficiency and reliability of our RL Science stack - the infrastructure, tooling, and systems that let researchers iterate quickly on training runs. As a Research Engineer on the team, you'll build and improve the core platform that underpins how we do RL at Anthropic, removing bottlenecks that slow down research and making it easier for the broader org to ship better models faster. This is high-leverage work: small improvements to velocity compound across every researcher and every run. Responsibilities Build and improve the RL training infrastructure that researchers depend on day-to-day Identify and remove bottlenecks across the RL stack: debugging, profiling, and rearchitecting where needed Partner closely with researchers and with adjacent engineering teams (inference, sandboxing, and many more) to understand pain points and ship tooling that makes them faster Own the reliability and performance of research runs end-to-end Contribute to design decisions that shape how Anthropic does RL at scale You may be a good fit if you Have strong software engineering fundamentals and a track record of building performant, reliable systems Have worked on ML infrastructure, distributed systems, or research tooling Care about enabling other people's work and find leverage through platforms rather than individual experiments Are comfortable operating across the stack, from low-level performance work to RL algorithms Have a bias toward shipping and iterating quickly, with a mix of high agency and low ego Strong candidates may also have Experience with large-scale distributed training (RL, pre-training, or post-training) Familiarity with JAX, PyTorch, or similar ML frameworks A track record of operating at the edge of research and infra in a fast-moving environment 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: £370,000 — £630,000 GBP 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. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. 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: Remote-Friendly (Travel-Required) | San Francisco, CA | New York City, NY 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 The RL Velocity team owns the efficiency and reliability of our RL Science stack - the infrastructure, tooling, and systems that let researchers iterate quickly on training runs. As a Research Engineer on the team, you'll build and improve the core platform that underpins how we do RL at Anthropic, removing bottlenecks that slow down research and making it easier for the broader org to ship better models faster. This is high-leverage work: small improvements to velocity compound across every researcher and every run. Responsibilities Build and improve the RL training infrastructure that researchers depend on day-to-day Identify and remove bottlenecks across the RL stack: debugging, profiling, and rearchitecting where needed Partner closely with researchers and with adjacent engineering teams (inference, sandboxing, and many more) to understand pain points and ship tooling that makes them faster Own the reliability and performance of research runs end-to-end Contribute to design decisions that shape how Anthropic does RL at scale You may be a good fit if you Have strong software engineering fundamentals and a track record of building performant, reliable systems Have worked on ML infrastructure, distributed systems, or research tooling Care about enabling other people's work and find leverage through platforms rather than individual experiments Are comfortable operating across the stack, from low-level performance work to RL algorithms Have a bias toward shipping and iterating quickly, with a mix of high agency and low ego Strong candidates may also have Experience with large-scale distributed training (RL, pre-training, or post-training) Familiarity with JAX, PyTorch, or similar ML frameworks A track record of operating at the edge of research and infra in a fast-moving environment 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: $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. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. 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: Europe, Remote About Zencoder At Zencoder.ai , we build and orchestrate AI agents that ship real work - code, research, operations, and more. What started as developer tooling is becoming a platform where people and agents collaborate across knowledge work tasks. The Opportunity We're looking for an experienced engineer to raise the bar on how our organization builds, tests, ships, and operates software - for both human engineers and AI coding agents. This is AI harness engineering: the discipline of making the fundamentals (CI/CD, testing, IaC, observability, secure delivery) so solid and well-shaped that agents can work productively inside them. Developer experience and platform engineering are a subset of that work. When the harness is good, humans ship faster and agents stop getting stuck on things that shouldn't be hard. Security is inseparable from this. AI agents are accelerating both the pace and the shape of vulnerabilities, and secure delivery has to be built into the same stream as everything else - not bolted on. Product teams own their services, pipelines, and tests. Your job is to raise the ceiling across the org - defining standards, building leverage, and helping teams level up their workflows, tooling, infrastructure, security posture, and necessary changes to get the most out of coding agents. Key Responsibilities This is a hands-on lead role - you'll be deep in the code, infra, and pipelines yourself. Shape the platform and infrastructure that everything else runs on - the foundations product teams build on top of; Improve the full developer loop: local development, remote agents, CI/CD, deployment, observability; Build shared tooling, templates, and golden paths that create real leverage - paved roads teams actually want to use; Stay ahead of the shifting security landscape as AI agents reshape both how vulnerabilities emerge and how they're exploited. Essential Qualifications Strong software engineering background - you will be building both tooling and managing core infrastructure in this role; Real fluency with AI coding agents - how they work, where they fail, and what makes them effective; A product mindset toward internal tooling - treating engineers and agents as users, and measuring whether the platform actually makes them faster; Deep experience with CI/CD, automated testing, IaC, Cloud and developer tooling; A passion for software security and secure delivery practices (shift-left / SecDevOps etc) in both the classical context and the new AI agent context. Why Join Zencoder? Impact AI is reshaping how work gets done, and you'll be at the forefront of that shift, building a product that enables teams to get more done with AI. We use our own products every day, so you'll see how your work lands right away. AI-First We use AI to expand what one person can accomplish. You'll constantly rethink how you work: what to hand over to AI, and where your own judgment matters most. Ownership We keep the team small and give people real scope, autonomy, and equity. You'll take initiative, make decisions, and see your work through to real outcomes, raising the bar with every iteration. High Talent Density A small team of highly capable people moves faster and builds better. You'll work directly with senior teammates and our founder, Andrew Filev, in a flat structure where ideas win on merit, not titles. Remote-First We hire globally, prioritizing talent over location, so you can work where you're most productive. Prefer in-person? We have a hub in Barcelona with weekly co-working. Zencoder is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

Location: London, England Fin , now part of Salesforce, is on a mission to help businesses provide perfect customer experiences. Our AI Agent Fin is the highest-performing AI Customer Agent on the market today, enabling businesses to deliver impeccable, always-on customer support across the customer journey, from service, to sales, to ecommerce. Powered by our own AI models, Fin resolves complex customer issues end-to-end across every channel, with minimal set-up and integration. Fin can also be combined with our natively integrated Intercom help desk, giving modern support teams one single system. Together with Salesforce, the #1 AI CRM, where humans with agents drive customer success, we're building the future of customer experience. Here, ambition meets action. Tech meets trust. And innovation isn't a buzzword, it's a way of life. The world of work as we know it is changing, and we're looking for Trailblazers who are passionate about bettering business and the world through AI. Ready to level up your career at the company leading workforce transformation in the agentic era? You're in the right place. Agentforce is the future of AI, and you are the future of Salesforce. What's the opportunity? We’re looking for Senior+ AI Infrastructure Engineers to build the systems that train and serve Fin's next generation of AI products. Fin is an AI company that builds from the GPU all the way up to a user agent that resolves millions of customer service queries a month. You’ll join a small, highly technical team working at the cutting edge of modern AI infrastructure. The AI Infra team built the training pipelines and runs the inference for custom models like Fin Apex, which outperforms frontier models in customer service tasks, and is the foundation of the AI Group's full stack approach to AI. We’re particularly interested in engineers who have: A track record of working on model training or model inference at scale , or on low‑level GPU coding (e.g. CUDA, Triton). Experience with one is great, multiple is even better. What will I be doing? As a Senior AI Infrastructure Engineer focused on model training and inference, you will: Implement and scale training pipelines for large transformer and LLM models, from data ingestion and preprocessing through distributed training and evaluation. Build and optimize inference services that deliver low‑latency, high‑reliability experiences for our customers, including autoscaling, routing, and fallbacks. Work on GPU‑level performance : tuning kernels, improving utilization, and identifying bottlenecks across our training and inference stack. Collaborate closely with ML scientists to implement cutting edge training and inference methods and bring them to production. Play an active role in hiring, mentoring, and developing other engineers on the team. Raise the bar for technical standards, reliability, and operational excellence across Fin's AI platform. Profile we’re looking for: These are indicative, not hard requirements We’re looking to hire Senior+ AI Infrastructure Engineers . You’re likely a great fit if: You have 5+ years of experience in software engineering , with a strong track record of shipping high‑quality products or platforms. You hold a degree in Computer Science, Computer Engineering, or a related field (or you have equivalent experience with very strong fundamentals). You have hands‑on experience with one or more of the following: Model training (especially transformers and LLMs). Model inference at scale (again, especially transformers and LLMs). Low‑level GPU work , such as writing CUDA or Triton kernels. Comfortable working in production environments at meaningful scale (traffic, data, or organizational). You communicate clearly, can explain complex technical topics to different audiences, and enjoy close collaboration with both engineers and non‑engineers. You take pride in strong technical fundamentals , love learning, and are willing to invest in your own development. Have deep knowledge of at least one programming language (for example Python, Ruby, Java, Go, etc.). Specific language experience is less important than your ability to write clean, reliable code and learn new stacks quickly. Bonus skills & attributes None of these are required, but they’re nice to have: Experience at AI native companies that train and/or run inference for their own models (e.g. modern AI labs or AI‑native product companies). Experience running training or inference workloads on Kubernetes . Experience with AWS or other major cloud providers. Production experience with Python in ML or infrastructure contexts. Demonstrated passion for technology through personal projects, open source, meetups, or publishing content about your work and learnings #LI-Hybrid Unleash Your Potential When you join Salesforce, you’ll be limitless in all areas of your life. Our benefits and resources support you to find balance and be your best, and our AI agents accelerate your impact so you can do your best. Together, we’ll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love. Apply today to not only shape the future — but to redefine what’s possible — for yourself, for AI, and the world. In the United States, compensation offered will be determined by factors such as location, job level, job-related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, and benefits. At Fin, we want to give people what they need to do the best work of their careers. Our benefits and programs are designed to support your health and wellbeing, your family, your time away from work, and your financial future. Offerings vary by location in line with local practices and requirements. Learn more about working at Fin and the benefits we offer at fin.ai/careers . Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records. Policies Fin has a hybrid working policy. We believe that working in person helps us stay connected, collaborate easier and create a great culture while still providing flexibility to work from home. We expect employees to be in the office at least three days per week. We have a radically open and accepting culture at Fin. We avoid spending time on divisive subjects to foster a safe and cohesive work environment for everyone. As an organization, our policy is to not advocate on behalf of the company or our employees on any social or political topics out of our internal or external communications. We respect personal opinion and expression on these topics on personal social platforms on personal time, and do not challenge or confront anyone for their views on non-work related topics. Our goal is to focus on doing incredible work to achieve our goals and unite the company through our core values . Salesforce is an equal opportunity employer and maintains a policy of non-discrimination with all employees and applicants for employment. What does that mean exactly? It means that at Salesforce, we believe in equality for all. And we believe we can lead the path to equality in part by creating a workplace that’s inclusive, and free from discrimination. Know your rights: workplace discrimination is illegal. Any employee or potential employee will be assessed on the basis of merit, competence and qualifications – without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law. This policy applies to current and prospective employees, no matter where they are in their Salesforce employment journey. It also applies to recruiting, hiring, job assignment, compensation, promotion, benefits, training, assessment of job performance, discipline, termination, and everything in between. Recruiting, hiring, and promotion decisions at Salesforce are fair and based on merit. The same goes for compensation, benefits, promotions, transfers, reduction in workforce, recall, training, and education.

Location: Dublin, Ireland Fin , now part of Salesforce, is on a mission to help businesses provide perfect customer experiences. Our AI Agent Fin is the highest-performing AI Customer Agent on the market today, enabling businesses to deliver impeccable, always-on customer support across the customer journey, from service, to sales, to ecommerce. Powered by our own AI models, Fin resolves complex customer issues end-to-end across every channel, with minimal set-up and integration. Fin can also be combined with our natively integrated Intercom help desk, giving modern support teams one single system. Together with Salesforce, the #1 AI CRM, where humans with agents drive customer success, we're building the future of customer experience. Here, ambition meets action. Tech meets trust. And innovation isn't a buzzword, it's a way of life. The world of work as we know it is changing, and we're looking for Trailblazers who are passionate about bettering business and the world through AI. Ready to level up your career at the company leading workforce transformation in the agentic era? You're in the right place. Agentforce is the future of AI, and you are the future of Salesforce. What's the opportunity? We’re looking for Senior+ AI Infrastructure Engineers to build the systems that train and serve Fin's next generation of AI products. Fin is an AI company that builds from the GPU all the way up to a user agent that resolves millions of customer service queries a month. You’ll join a small, highly technical team working at the cutting edge of modern AI infrastructure. The AI Infra team built the training pipelines and runs the inference for custom models like Fin Apex, which outperforms frontier models in customer service tasks, and is the foundation of the AI Group's full stack approach to AI. We’re particularly interested in engineers who have: A track record of working on model training or model inference at scale , or on low‑level GPU coding (e.g. CUDA, Triton). Experience with one is great, multiple is even better. What will I be doing? As a Senior AI Infrastructure Engineer focused on model training and inference, you will: Implement and scale training pipelines for large transformer and LLM models, from data ingestion and preprocessing through distributed training and evaluation. Build and optimize inference services that deliver low‑latency, high‑reliability experiences for our customers, including autoscaling, routing, and fallbacks. Work on GPU‑level performance : tuning kernels, improving utilization, and identifying bottlenecks across our training and inference stack. Collaborate closely with ML scientists to implement cutting edge training and inference methods and bring them to production. Play an active role in hiring, mentoring, and developing other engineers on the team. Raise the bar for technical standards, reliability, and operational excellence across Fin's AI platform. Profile we’re looking for: These are indicative, not hard requirements We’re looking to hire Senior+ AI Infrastructure Engineers . You’re likely a great fit if: You have 5+ years of experience in software engineering , with a strong track record of shipping high‑quality products or platforms. You hold a degree in Computer Science, Computer Engineering, or a related field (or you have equivalent experience with very strong fundamentals). You have hands‑on experience with one or more of the following: Model training (especially transformers and LLMs). Model inference at scale (again, especially transformers and LLMs). Low‑level GPU work , such as writing CUDA or Triton kernels. Comfortable working in production environments at meaningful scale (traffic, data, or organizational). You communicate clearly, can explain complex technical topics to different audiences, and enjoy close collaboration with both engineers and non‑engineers. You take pride in strong technical fundamentals , love learning, and are willing to invest in your own development. Have deep knowledge of at least one programming language (for example Python, Ruby, Java, Go, etc.). Specific language experience is less important than your ability to write clean, reliable code and learn new stacks quickly. Bonus skills & attributes None of these are required, but they’re nice to have: Experience at AI native companies that train and/or run inference for their own models (e.g. modern AI labs or AI‑native product companies). Experience running training or inference workloads on Kubernetes . Experience with AWS or other major cloud providers. Production experience with Python in ML or infrastructure contexts. Demonstrated passion for technology through personal projects, open source, meetups, or publishing content about your work and learnings #LI-Hybrid Unleash Your Potential When you join Salesforce, you’ll be limitless in all areas of your life. Our benefits and resources support you to find balance and be your best, and our AI agents accelerate your impact so you can do your best. Together, we’ll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love. Apply today to not only shape the future — but to redefine what’s possible — for yourself, for AI, and the world. In the United States, compensation offered will be determined by factors such as location, job level, job-related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, and benefits. At Fin, we want to give people what they need to do the best work of their careers. Our benefits and programs are designed to support your health and wellbeing, your family, your time away from work, and your financial future. Offerings vary by location in line with local practices and requirements. Learn more about working at Fin and the benefits we offer at fin.ai/careers . Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records. Policies Fin has a hybrid working policy. We believe that working in person helps us stay connected, collaborate easier and create a great culture while still providing flexibility to work from home. We expect employees to be in the office at least three days per week. We have a radically open and accepting culture at Fin. We avoid spending time on divisive subjects to foster a safe and cohesive work environment for everyone. As an organization, our policy is to not advocate on behalf of the company or our employees on any social or political topics out of our internal or external communications. We respect personal opinion and expression on these topics on personal social platforms on personal time, and do not challenge or confront anyone for their views on non-work related topics. Our goal is to focus on doing incredible work to achieve our goals and unite the company through our core values . Salesforce is an equal opportunity employer and maintains a policy of non-discrimination with all employees and applicants for employment. What does that mean exactly? It means that at Salesforce, we believe in equality for all. And we believe we can lead the path to equality in part by creating a workplace that’s inclusive, and free from discrimination. Know your rights: workplace discrimination is illegal. Any employee or potential employee will be assessed on the basis of merit, competence and qualifications – without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law. This policy applies to current and prospective employees, no matter where they are in their Salesforce employment journey. It also applies to recruiting, hiring, job assignment, compensation, promotion, benefits, training, assessment of job performance, discipline, termination, and everything in between. Recruiting, hiring, and promotion decisions at Salesforce are fair and based on merit. The same goes for compensation, benefits, promotions, transfers, reduction in workforce, recall, training, and education.

Location: London Workplace: Hybrid We are looking for an AI Infra engineer to join our growing team. We work with Kubernetes, Slurm, Python, C++, PyTorch, and primarily on AWS. As an AI Infrastructure Engineer, you will be partnering closely with our Inference and Research teams to build, deploy, and optimize our large-scale AI training and inference clusters. RESPONSIBILITIES - Design, deploy, and maintain scalable Kubernetes clusters for AI model inference and training workloads - Manage and optimize Slurm-based HPC environments for distributed training of large language models - Develop robust APIs and orchestration systems for both training pipelines and inference services - Implement resource scheduling and job management systems across heterogeneous compute environments - Benchmark system performance, diagnose bottlenecks, and implement improvements across both training and inference infrastructure - Build monitoring, alerting, and observability solutions tailored to ML workloads running on Kubernetes and Slurm - Respond swiftly to system outages and collaborate across teams to maintain high uptime for critical training runs and inference services - Optimize cluster utilization and implement autoscaling strategies for dynamic workload demands QUALIFICATIONS - Strong expertise in Kubernetes administration, including custom resource definitions, operators, and cluster management - Hands-on experience with Slurm workload management, including job scheduling, resource allocation, and cluster optimization - Experience with deploying and managing distributed training systems at scale - Deep understanding of container orchestration and distributed systems architecture - High level familiarity with LLM architecture and training processes (Multi-Head Attention, Multi/Grouped-Query, distributed training strategies) - Experience managing GPU clusters and optimizing compute resource utilization REQUIRED SKILLS - Expert-level Kubernetes administration and YAML configuration management - Proficiency with Slurm job scheduling, resource management, and cluster configuration - Python and C++ programming with focus on systems and infrastructure automation - Hands-on experience with ML frameworks such as PyTorch in distributed training contexts - Strong understanding of networking, storage, and compute resource management for ML workloads - Experience developing APIs and managing distributed systems for both batch and real-time workloads - Solid debugging and monitoring skills with expertise in observability tools for containerized environments PREFERRED SKILLS - Experience with Kubernetes operators and custom controllers for ML workloads - Advanced Slurm administration including multi-cluster federation and advanced scheduling policies - Familiarity with GPU cluster management and CUDA optimization - Experience with other ML frameworks like TensorFlow or distributed training libraries - Background in HPC environments, parallel computing, and high-performance networking - Knowledge of infrastructure as code (Terraform, Ansible) and GitOps practices - Experience with container registries, image optimization, and multi-stage builds for ML workloads REQUIRED EXPERIENCE - Demonstrated experience managing large-scale Kubernetes deployments in production environments - Proven track record with Slurm cluster administration and HPC workload management - Previous roles in SRE, DevOps, or Platform Engineering with focus on ML infrastructure - Experience supporting both long-running training jobs and high-availability inference services - Ideally, 3-5 years of relevant experience in ML systems deployment with specific focus on cluster orchestration and resource management

Location: United States & Canada Workplace: Remote About WorkOS 🚀 WorkOS builds modern developer tools and APIs that make it easy for companies to become Enterprise Ready. Our platform powers authentication, identity, authorization, and other critical infrastructure that developers need to securely scale their products to large organizations. We recently raised a $100M Series C, valuing the company at $2B, led by Meritech and Sapphire with participation from Greenoaks, Craft, Abstract, and Audacious. WorkOS powers enterprise features for many of the fastest-growing AI companies, including OpenAI, Cursor, and Perplexity, Sierra, and Plaid. As AI reshapes software, WorkOS is at the frontier of Human and Agent Authentication, Identity, and Access Control helping companies answer a new critical question: who are your agents, and what are they allowed to do? Our fast-growing customer base includes hundreds of modern software companies building the next generation of enterprise-ready products. ABOUT THE ROLE We're looking for a GTM Engineer to join our team. This is a hands-on engineering role embedded in our GTM org. You'll own the systems that power how we identify, enrich, score, and engage prospects at scale: a custom built enrichment pipelines leveraging Cargo, the campaign sync tooling that feeds our events and outbound plays, and the code that ties it all together. You'll work at the intersection of GTM strategy and software engineering by writing production TypeScript/Python, shipping PRs, designing schemas, and building internal tools that the whole GTM team depends on daily. Think of it as being the engineer who makes the rest of the GTM team unreasonably effective. You don't need to have done this exact job before. You need to care about the craft. The best GTM Engineers at WorkOS are the ones who spot a broken workflow in Slack at 11pm, open a PR the next morning, and ship the fix before the standup. They're the ones who, when the sales team complains about bad data, don't just route it to RevOps, they dig into the Snowflake schema and find the upstream issue. If that sounds like how you work, we want to talk.   RESPONSIBILITIES - Enrichment infrastructure: You'll maintain and improve our core enrichment pipeline: the workflows that take a new signup or a conference attendee list and return a fully enriched, scored, and Salesforce-upserted record. Today that involves Cargo, Clay, Snowflake, and a growing set of data providers (Apollo, Linkup, Exa). You'll own the reliability, fill rates, and schema consistency of these systems. - Account scoring: You'll work on the fit scoring pipeline: the logic that evaluates every company flowing through our stack against our ICP and writes results to Snowflake and SFDC. This includes batching optimization, retry logic, logging and observability, and continuously improving the underlying signals. - Campaign and event tooling: You'll build and maintain the systems that sync event attendee lists (Luma, conference CSVs) through enrichment and into Salesforce campaigns. When a BDR asks "who came to our event last night?", your tooling answers that question automatically and routes the right records to the right owners. You’ll also propose and prioritize growth experiments, validate the approach, own implementation, measure results, and iterate. - Custom Tooling integration: You'll build and maintain enrichment tooling accessible via Wallaby, WorkOS's internal AI agent that the GTM team uses to look up prospects, run ad-hoc enrichments, and surface account intel on demand. You’ll design the underlying tools so they’re clean, effective, and composable, using AI and automation across research, enrichment, routing, and engagement workflows. - GTM data infra: You'll work closely with the data engineering team to keep our Snowflake schema in sync with the tools that consume it, track downstream pipeline impacts when schemas change, and ensure the data flowing into SFDC is accurate and up-to-date. Own and maintain lead and conversion tracking across the funnel, including lifecycle stages and campaign attribution. Keep the underlying data and tracking reliable so we can measure conversion and identify where prospects drop off. - Code: You'll ship real code. PRs go through review. You'll debug production incidents (Salesforce API rate limits, Cargo timeouts, enrichment failures). You'll instrument logging, add observability, and think carefully about batching, concurrency, and idempotency. QUALIFICATIONS - Engineering foundation: You're a capable software engineer. You write clean TypeScript or Python, understand async patterns, know how to design a database schema, and can debug a production incident from logs alone. You don't need to have worked at a software company, but you need to be able to write and ship code. - GTM domain knowledge: You've worked with the tools: Salesforce, HubSpot, Clay, Cargo, Apollo, or equivalents. You understand how leads flow through a CRM, what enrichment providers actually return, and why data quality degrades over time. Ideally you've had a BDR or AE background that gives you intuition for what the sales team actually needs. - Systems thinking: You think in pipelines, not one-off scripts. When you build something, you think about what happens at 10x volume, when an API goes down, or when the same company gets submitted five times simultaneously. - 3+ years of experience in GTM Engineering, Revenue Operations, or a closely adjacent technical role at a high-growth B2B SaaS company. Benefits and Perks (US Only) 💖 At WorkOS, we offer resources that emphasize personal and familial well-being. We offer healthcare coverage for you and your family, including medical, dental, and vision. We offer parental leave, paid-time off and fully remote working arrangements.   - 401k matching - Competitive Equity - Healthcare, dental and vision coverage - FSA, ST/LT Disability, Voluntary Life - Carrot fertility benefits - 20 days paid vacation + 10 holidays + unlimited sick leave - 12 weeks fully paid parental leave - Fitness: Monthly stipend for gyms, yoga classes, race registrations or whatever keeps you active - Wellness: Monthly stipend for a massage, meditations class, therapy, or activities that enhance your well-being - Commuter benefits for hybrid employees in SF/NYC - Unlimited token usage! Please inquire directly with our recruiting team for benefits available to those working outside the US.   Equal Opportunity Employer WorkOS is an equal opportunity employer, committed to diversity and inclusiveness. We will consider all qualified applicants without regard to race, color, nationality, gender, gender identity or expression, sexual orientation, religion, disability or age. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

Location: Singapore About Workato Workato is the leading Control and Execution Platform for Enterprise AI — the neutral platform enterprises trust to put AI to work across their business. Workato unifies data, applications, and processes into a single platform so AI can reliably orchestrate business processes in production at enterprise scale. Built on more than a decade of running mission-critical processes for over half the Fortune 500 — including Nasdaq, Amazon, Cisco, Vodafone, Atlassian, and Lucid Motors — Workato turns over 14,000 enterprise systems AI needs to act on into one governed execution layer. For more information, visit workato.com . Why join us? Ultimately, Workato believes in fostering a flexible, trust-oriented culture that empowers everyone to take full ownership of their roles . We are driven by innovation and looking for team players who want to actively build our company. But, we also believe in balancing productivity with self-care . That’s why we offer all of our employees a vibrant and dynamic work environment along with a multitude of benefits they can enjoy inside and outside of their work lives. If this sounds right up your alley, please submit an application. We look forward to getting to know you! Also, feel free to check out why: Business Insider named us an “enterprise startup to bet your career on” Forbes’ Cloud 100 recognized us as one of the top 100 private cloud companies in the world Deloitte Tech Fast 500 ranked us as the 17th fastest growing tech company in the Bay Area, and 96th in North America Quartz ranked us the #1 best company for remote workers We are looking for a Senior Software Engineer (Golang) to join our Platform team . Our platform is a large-scale system combining a Ruby on Rails monolith with a growing ecosystem of Go microservices. In this role, you will build and maintain business logic and enterprise-grade features for the Workato platform, including on-premise features, authentication, caching, work queuing, and APIs. You will also be expected to: • Design, build, and maintain Go microservices with a focus on correctness and reliability. • Implement business logic and enterprise-quality standards across the platform. • Collaborate with Infra and SRE teams to ensure high availability and scalability requirements are met. • Contribute across all phases of the development lifecycle. • Write well-designed, testable, and efficient code. • Evaluate and propose improvements to existing systems. Product & Technology Context: Strong web development background, with experience building user-facing products — including UI, business logic, and integrations with external APIs. Product-minded engineering experience: you understand how technology ties back to user and business needs, not just infrastructure. Track record of shipping user-facing business features across product platforms, developer experience platforms, or other customer-facing surfaces. Solid production experience — typically 7+ years — with the depth and maturity to build and support systems in production. Clear understanding of how web applications work end-to-end, and how the different pieces fit together to deliver a product or business outcome. Depth and consistency in technical background — we value engineers who've built real expertise over time, rather than hopping across many different stacks. Our core platform is a Ruby on Rails monolith, with some Go services handling more focused functions alongside the Ruby/monolith team. Experience with these is a plus, but we care most about strong engineering fundamentals and product experience. This role isn't primarily ML/LLM-focused, so candidates whose recent experience has been mostly in ML/LLM work may not be the strongest fit here. Required • 7+ years of industry experience in software engineering. • Solid experience with Go on the backend (2+ years). • Good debugging, analytical, and problem-solving skills. • Understanding of core building blocks of web applications: databases, load balancers, application servers, message brokers, caching, monitoring, etc.. • Experience working with distributed systems. • Familiarity with version control (Git) and CI/CD practices. Nice to Have • Experience with Ruby on Rails. • Experience building B2B systems. • Experience in related fields (DevOps, ML, DBA, Enterprise applications, etc.). • Experience with third-party REST APIs at scale (request throttling, batch processing, etc.). • Contributions to open-source projects. Soft Skills / Personal Characteristics • Readiness to work with teams distributed across multiple timezones. • Strong spoken and written English. • Curiosity and openness toward new technologies; willingness to experiment. • Ownership mindset — comfortable taking features from design through to production and maintenance. REQ ID: 2423