Location: Home based - EMEA
Canonical is a leading provider of open source software and operating systems to the global enterprise and technology markets. Our platform, Ubuntu, is very widely used in breakthrough enterprise initiatives such as public cloud, data science, AI, engineering innovation, and IoT. Our customers include the world's leading public cloud and silicon providers, and industry leaders in many sectors. The company is a pioneer of global distributed collaboration, with 1200+ colleagues in 75+ countries and very few office-based roles. Teams meet two to four times yearly in person, in interesting locations around the world, to align on strategy and execution.
The company is founder-led, profitable, and growing.
We are hiring multiple positions from early-career engineers to experienced architects focused on the different dimensions of dependable software: cybersecurity, quality, and safety.
What does it take to bring the world’s open source software into mission-critical systems? At Canonical, we are driving the engineering discipline needed to build secure, reliable, and high-quality products that our automotive and industrial partners can depend on. Ubuntu is already the world’s most widely used Linux distribution in general; we want to make it the world’s best choice for the automotive and industrial sectors too.
We operate at the boundary between upstream open source innovation and the industrial regulatory realities. Our engineers define the concepts, architectures, processes, verification strategies, and certification artifacts to transform a general-purpose platform into a dependable, certifiable foundation for automotive and industrial compute. This is systems-level engineering at scale, requiring precision, rigor, and a deep understanding of how complex software behaves in constrained, regulated environments.
We are looking for engineers and architects who combine an open source hacker and builder mindset with respect for rigorous, standards-driven engineering. You will work across Canonical engineering teams and with leading industrial partners, helping to build this capability from the ground up and shaping how open source enters the most demanding technical domains.
Location
: This is a remote role based in the EMEA region.
The role entails
Translate automotive and industrial cybersecurity, quality and safety standards into actionable engineering requirements and perform gap analyses against current capabilities.
Drive the creation of structured engineering artifacts, including requirements, traceability models, risk analysis, test strategies, and certification documentation.
Collaborate with Canonical engineering teams to align development practices with industrial compliance needs.
Design, implement, and execute verification and validation activities required to qualify Ubuntu and other components for regulated environments.
Develop and improve automation and tooling to scale traceability, testing, compliance evidence generation, and reporting.
Engage with industrial partners to understand their expectations, align external expectations and internal capabilities, and support audits and technical reviews.
Contribute to the continuous improvement of Canonical’s internal processes and standards capability as the team scales.
What we are looking for in you
Strong software engineering foundation with experience in developing complex systems on Linux.
Professional experience with C, C++, and Python in production environments.
Demonstrated experience working within automotive, industrial, or other regulated environments governed by safety, cybersecurity, or quality standards (such as ISO 26262, ISO/SAE 21434, IEC 61508, IEC 62443, SOTIF, or (A)SPICE), and applying them in practice.
Experience in system or software architecture, implementation, or verification and validation of complex embedded systems.
Strong written and verbal communication skills, with the ability to produce precise technical documentation.
Ability to collaborate effectively in a distributed team and engage with industrial partners.
Additional experience that is valuable
Contributions to open source projects, particularly in the area of Software Defined Vehicles.
Knowledge of Debian packaging.
Experience with Rust or safety-critical software development.
What we offer colleagues
We consider geographical location, experience, and performance in shaping compensation worldwide. We revisit compensation annually (and more often for graduates and associates) to ensure we recognize outstanding performance. In addition to base pay, we offer a performance-driven annual bonus. We provide all team members with additional benefits which reflect our values and ideals. We balance our programs to meet local needs and ensure fairness globally.
Distributed work environment with twice-yearly team sprints in person
Personal learning and development budget of USD 2,000 per year
Annual compensation review
Recognition rewards
Annual holiday leave
Maternity and paternity leave
Team Member Assistance Program & Wellness Platform
Opportunity to travel to new locations to meet colleagues
Priority Pass and travel upgrades for long-haul company events
About Canonical
Canonical is a pioneering tech firm at the forefront of the global move to open source. As the company that publishes Ubuntu, one of the most important open-source projects and the platform for AI, IoT, and the cloud, we are changing the world of software. We recruit on a global basis and set a very high standard for people joining the company. We expect excellence - in order to succeed, we need to be the best at what we do. Most colleagues at Canonical have worked from home since its inception in 2004. Working here is a step into the future and will challenge you to think differently, work smarter, learn new skills, and raise your game.
Canonical is an equal-opportunity employer
We are proud to foster a workplace free from discrimination. Diversity of experience, perspectives, and background create a better work environment and better products.
Whatever your identity we will give your application fair consideration
.
Location: San Francisco; Palo Alto; New York City
We build and run the inference engine behind every Perplexity query and deploy dozens of model architectures at scale with tight latency and cost budgets. Our stack is Rust, Python, CUDA, and CuTe DSL - and we need another engineer to join us.
WHAT YOU WILL WORK ON
Examples of real work the team does:
- New models support. Support transformer-based retrieval, text-generation, and multimodal models in our inference infrastructure, from weight loading, request scheduling and KV-cache management to support in API Gateway.
- GPU kernels migration to CuTe DSL. Port our in-house CUDA kernels to NVIDIA's CuTe DSL so they run on GB200 today and are portable to Vera Rubin racks tomorrow.
- Rust-native serving runtime. Develop our internal Rust-based inference server to solve all Python pains and keep up with rapidly growing traffic.
- Performance optimisation. Profile and fix bottlenecks from network ingress through continuous batching and GPU kernel interleaving.
- Reliability and observability. Build dashboards, alerts, and automated remediation so we catch regressions before users do. Respond to and learn from production incidents.
WHO WE'RE LOOKING FOR
- Deep experience with GPU programming and performance work (CUDA, Triton, CUTLASS, or similar). Any other deep systems programming experience is a plus.
- You understand modern LLM architectures and are able to bring them up reliably in a production environment.
- You've built and operated production distributed systems under real load - ideally performance-critical ones.
- Comfortable working across languages and layers: Rust for the serving runtime, Python for model code, CUDA/CuteDSL for kernels.
- You own problems end-to-end. You can read a research paper on Monday, write a kernel on Wednesday, and debug a production incident on Friday.
- Self-directed. You do well in fast-moving environments where the path forward isn't laid out for you.
GOOD IF YOU TOUCHED ANY OF
- ML compilers and framework internals: PyTorch internals, torch.compile, custom operators.
- Distributed GPU communication: NCCL, NVLink, InfiniBand, RDMA libraries, model/tensor parallelism.
- Low-precision inference: INT8/FP8/FP4 quantization, mixed-precision serving.
- Profiling and debugging tools: Nsight Compute/Systems, CUDA-GDB, PTX/SASS analysis.
- Container orchestration: Kubernetes, GPU scheduling, autoscaling inference workloads.
QUALIFICATIONS
- 3+ years of professional software engineering experience with meaningful work on ML inference or high-performance systems.
- Familiarity with at least one deep learning framework (PyTorch, JAX, TensorFlow).
- Understanding of GPU architectures (memory hierarchy, warp scheduling, tensor cores).
- Understanding of common LLM architectures and inference optimization techniques (e.g. quantization, speculative decoding, prefill-decode disaggregation).
Location: San Francisco; Palo Alto; New York City
We build and run the inference engine behind every Perplexity query and deploy dozens of model architectures at scale with tight latency and cost budgets. Our stack is Rust, Python, CUDA, and CuTe DSL - and we need another engineer to join us.
WHAT YOU WILL WORK ON
Examples of real work the team does:
- New models support. Support transformer-based retrieval, text-generation, and multimodal models in our inference infrastructure, from weight loading, request scheduling and KV-cache management to support in API Gateway.
- GPU kernels migration to CuTe DSL. Port our in-house CUDA kernels to NVIDIA's CuTe DSL so they run on GB200 today and are portable to Vera Rubin racks tomorrow.
- Rust-native serving runtime. Develop our internal Rust-based inference server to solve all Python pains and keep up with rapidly growing traffic.
- Performance optimisation. Profile and fix bottlenecks from network ingress through continuous batching and GPU kernel interleaving.
- Reliability and observability. Build dashboards, alerts, and automated remediation so we catch regressions before users do. Respond to and learn from production incidents.
WHO WE'RE LOOKING FOR
- Deep experience with GPU programming and performance work (CUDA, Triton, CUTLASS, or similar). Any other deep systems programming experience is a plus.
- You understand modern LLM architectures and are able to bring them up reliably in a production environment.
- You've built and operated production distributed systems under real load - ideally performance-critical ones.
- Comfortable working across languages and layers: Rust for the serving runtime, Python for model code, CUDA/CuteDSL for kernels.
- You own problems end-to-end. You can read a research paper on Monday, write a kernel on Wednesday, and debug a production incident on Friday.
- Self-directed. You do well in fast-moving environments where the path forward isn't laid out for you.
GOOD IF YOU TOUCHED ANY OF
- ML compilers and framework internals: PyTorch internals, torch.compile, custom operators.
- Distributed GPU communication: NCCL, NVLink, InfiniBand, RDMA libraries, model/tensor parallelism.
- Low-precision inference: INT8/FP8/FP4 quantization, mixed-precision serving.
- Profiling and debugging tools: Nsight Compute/Systems, CUDA-GDB, PTX/SASS analysis.
- Container orchestration: Kubernetes, GPU scheduling, autoscaling inference workloads.
QUALIFICATIONS
- 3+ years of professional software engineering experience with meaningful work on ML inference or high-performance systems.
- Familiarity with at least one deep learning framework (PyTorch, JAX, TensorFlow).
- Understanding of GPU architectures (memory hierarchy, warp scheduling, tensor cores).
- Understanding of common LLM architectures and inference optimization techniques (e.g. quantization, speculative decoding, prefill-decode disaggregation).
Location: London
We are looking for an AI Inference Engineer to join our growing team. We build and run the inference engine behind every Perplexity query and deploy dozens of model architectures at scale with tight latency and cost budgets. Our stack is Rust, Python, CUDA, and CuTe DSL.
RESPONSIBILITIES:
- New models support. Support transformer-based retrieval, text-generation, and multimodal models in our inference infrastructure, from weight loading, request scheduling and KV-cache management to support in API Gateway.
- GPU kernels migration to CuTe DSL. Port our in-house CUDA kernels to NVIDIA's CuTe DSL so they run on GB200 today and are portable to Vera Rubin racks tomorrow.
- Rust-native serving runtime. Develop our internal Rust-based inference server to solve all Python pains and keep up with rapidly growing traffic.
- Performance optimisation. Profile and fix bottlenecks from network ingress through continuous batching and GPU kernels interleaving.
- Reliability and observability. Build dashboards, alerts, and automated remediation so we catch regressions before users do. Respond to and learn from production incidents.
WHO WE'RE LOOKING FOR:
- Deep experience with GPU programming and performance work (CUDA, Triton, CUTLASS, or similar). Any other deep systems programming experience is a plus.
- You understand modern LLM architectures and are able to bring them up reliably in a production environment.
- You've built and operated production distributed systems under real load - ideally performance-critical ones.
- Comfortable working across languages and layers: Rust for the serving runtime, Python for model code, CUDA/CuteDSL for kernels.
- You own problems end-to-end. You can read a research paper on Monday, write a kernel on Wednesday, and debug a production incident on Friday.
- Self-directed. You do well in fast-moving environments where the path forward isn't laid out for you.
NICE-TO-HAVE:
- ML compilers and framework internals: PyTorch internals, torch.compile, custom operators.
- Distributed GPU communication: NCCL, NVLink, InfiniBand, RDMA libraries, model/tensor parallelism.
- Low-precision inference: INT8/FP8/FP4 quantization, mixed-precision serving.
- Profiling and debugging tools: Nsight Compute/Systems, CUDA-GDB, PTX/SASS analysis.
- Container orchestration: Kubernetes, GPU scheduling, autoscaling inference workloads.
QUALIFICATIONS:
- 3+ years of professional software engineering experience with meaningful work on ML inference or high-performance systems.
- Familiarity with at least one deep learning framework (PyTorch, JAX, TensorFlow).
- Understanding of GPU architectures (memory hierarchy, warp scheduling, tensor cores).
- Understanding of common LLM architectures and inference optimization techniques (e.g. quantization, speculative decoding, prefill-decode disaggregation).
Final offer amounts are determined by multiple factors including experience and expertise.
Equity: In addition to the base salary, equity may be part of the total compensation package.
Location: London
We are looking for an AI Inference Engineer to join our growing team. We build and run the inference engine behind every Perplexity query and deploy dozens of model architectures at scale with tight latency and cost budgets. Our stack is Rust, Python, CUDA, and CuTe DSL.
RESPONSIBILITIES:
- New models support. Support transformer-based retrieval, text-generation, and multimodal models in our inference infrastructure, from weight loading, request scheduling and KV-cache management to support in API Gateway.
- GPU kernels migration to CuTe DSL. Port our in-house CUDA kernels to NVIDIA's CuTe DSL so they run on GB200 today and are portable to Vera Rubin racks tomorrow.
- Rust-native serving runtime. Develop our internal Rust-based inference server to solve all Python pains and keep up with rapidly growing traffic.
- Performance optimisation. Profile and fix bottlenecks from network ingress through continuous batching and GPU kernels interleaving.
- Reliability and observability. Build dashboards, alerts, and automated remediation so we catch regressions before users do. Respond to and learn from production incidents.
WHO WE'RE LOOKING FOR:
- Deep experience with GPU programming and performance work (CUDA, Triton, CUTLASS, or similar). Any other deep systems programming experience is a plus.
- You understand modern LLM architectures and are able to bring them up reliably in a production environment.
- You've built and operated production distributed systems under real load - ideally performance-critical ones.
- Comfortable working across languages and layers: Rust for the serving runtime, Python for model code, CUDA/CuteDSL for kernels.
- You own problems end-to-end. You can read a research paper on Monday, write a kernel on Wednesday, and debug a production incident on Friday.
- Self-directed. You do well in fast-moving environments where the path forward isn't laid out for you.
NICE-TO-HAVE:
- ML compilers and framework internals: PyTorch internals, torch.compile, custom operators.
- Distributed GPU communication: NCCL, NVLink, InfiniBand, RDMA libraries, model/tensor parallelism.
- Low-precision inference: INT8/FP8/FP4 quantization, mixed-precision serving.
- Profiling and debugging tools: Nsight Compute/Systems, CUDA-GDB, PTX/SASS analysis.
- Container orchestration: Kubernetes, GPU scheduling, autoscaling inference workloads.
QUALIFICATIONS:
- 3+ years of professional software engineering experience with meaningful work on ML inference or high-performance systems.
- Familiarity with at least one deep learning framework (PyTorch, JAX, TensorFlow).
- Understanding of GPU architectures (memory hierarchy, warp scheduling, tensor cores).
- Understanding of common LLM architectures and inference optimization techniques (e.g. quantization, speculative decoding, prefill-decode disaggregation).
Final offer amounts are determined by multiple factors including experience and expertise.
Equity: In addition to the base salary, equity may be part of the total compensation package.
Location: Japan; Remote; Singapore
About the role
We are seeking a hands-on Security Engineer who thrives in a startup environment.
You'll work alongside product owners and engineers with the objective to secure the products in Startale's ecosystem. Products include a next-generation decentralized exchange with a fully on-chain order book (Strium), a user-facing application (StartaleApp) and a stablecoin (USDSC). This is a
hands-on, technical role
. You'll be the person who actively tests our systems, hunts for vulnerabilities, models threats against our products, and works with engineers to close the gaps — not the person who writes policies and generates reports. You'll report to the Security Lead and collaborate daily with Backend, Frontend, DevOps, and Blockchain engineering teams.
Why this role
Startale's products handle user funds and on-chain transactions so security work has tangible impact.
Owning the security posture of a project at scale and complexity such as Strium is an opportunity for professional growth.
You will have direct influence over how product security is built across the organization.
Focus is on driving product security and not on maintaining compliance documentation.
Our team is backed by and partnering with leading Japanese enterprises so you will have a chance to work in a stable and well-funded company but with the autonomy and speed of a small team.
Key responsibilities
Security Assessments & Penetration Testing
: Conduct hands-on security testing of our applications, APIs, and infrastructure. Simulate real attack scenarios against our products. Find the vulnerabilities before external attackers or whitehat researchers do. Work with engineers to fix issues pragmatically.
Threat Modeling
: Build threat models for new services and features — especially Strium's trading engine, order book, and transaction flows. Identify attack surfaces, model adversary behavior, and define what needs to be hardened before launch.
Vulnerability Triage & Remediation
: Own the end-to-end lifecycle of findings — from discovery through severity assessment, developer-facing write-ups, remediation guidance, and verification of fixes. Coordinate with engineers so issues actually get closed.
Vulnerability Disclosure & Bug Bounty
: Manage incoming whitehat reports, validate findings by reproducing them, assess severity, communicate with researchers.
AI Tools Security Support
: Assess technical risks related to AI tools used within teams (such as data exfiltration, prompt injection, training-on-input), maintain security baselines for AI coding tools and review AI-powered internal tools.
Qualifications
Must-have
5+ years of hands-on experience with a focus on application security, penetration testing, or product security.
Demonstrated ability to find vulnerabilities — through manual testing, architecture and/or code review, or creative attack simulation. You should be able to describe specific bugs you've found and how you found them.
Practical experience with
exchange or trading platform security
— from a DEX (preferred) or DeFi protocol. You should understand order book mechanics, transaction flows, wallet security, and the threat landscape specific to trading infrastructure.
Scripting and automation ability — you write tools and automate to scale security across the stack, not just audit and write reports.
Experience triaging vulnerabilities and writing clear, actionable remediation guidance for developers.
Strong written communication in English — you'll write tickets, assessment reports and researcher responses.
Strong plus
Experience with cloud infrastructure security — least-privilege enforcement, network security, secrets management.
Experience with container security — network policies, RBAC, pod security standards, image scanning, Dockerfile hardening, base image management.
Ability to read and review code in at least one of: TypeScript/JavaScript, Solidity, Rust.
Understanding of software supply chain security, including dependency risks, build integrity, and methods for tracking what components are included in shipped software.
Experience managing or participating in a bug bounty program (e.g. Immunefi, HackerOne).
Domain plus
Experience securing
AI/LLM tooling
in engineering teams — prompt injection risks, data leakage prevention, tool configuration hardening
Japanese language ability (not required, but useful for company context)
Location / Timezone
Strong preference for Tokyo-based or Singapore-based candidates - Startale office locations.
Remote-friendly for exceptional candidates — must have 3+ hours overlap with Tokyo business hours (JST, UTC+9).
Target start date
As soon as available; realistic target start of Q3 2026.
Target companies
Decentralized exchanges, DeFi protocols, blockchain security firms, L1/L2 chain security teams, or fintech companies with trading infrastructure. We're also open to strong AppSec engineers from cloud-native startups who have genuine interest in web3.
Ideal candidate
You're a hands-on security engineer who finds real vulnerabilities, not just runs scanners. You've secured a trading platform or exchange and understand the threats specific to financial infrastructure — order book manipulation, transaction signing, wallet compromise, front-running. You can take a system, map the attack surface, and come back with findings that matter. You write clear reports that engineers act on, and you know the difference between a theoretical risk and a real one. You're comfortable working independently in a fast-moving team where there's no playbook — you write the playbook.
Location: Hybrid
About Us
At Cloudflare, we are on a mission to help build a better Internet. Today the company runs one of the world’s largest networks that powers millions of websites and other Internet properties for customers ranging from individual bloggers to SMBs to Fortune 500 companies. Cloudflare protects and accelerates any Internet application online without adding hardware, installing software, or changing a line of code. Internet properties powered by Cloudflare all have web traffic routed through its intelligent global network, which gets smarter with every request. As a result, they see significant improvement in performance and a decrease in spam and other attacks. Cloudflare was named to Entrepreneur Magazine’s Top Company Cultures list and ranked among the World’s Most Innovative Companies by Fast Company.
At Cloudflare, we’re not looking for people who wait for a polished roadmap; we’re looking for the builders who see the cracks in the Internet that everyone else has simply learned to live with. We value candidates who have the instinct to spot a "normalized" problem and the AI-native curiosity to create a solution using the latest tools. Our culture is built on iteration, leveraging AI to ship faster today to make it better tomorrow, while ensuring that every improvement, no matter how small, is shared across the team to lift everyone up. If you’re the type of person who values curiosity over bureaucracy, and that AI is a partner in solving tough problems to keep the Internet moving forward, you’ll fit right in.
Available Locations
Austin, US
Lisbon, Portugal
London, UK
NYC, US
About the Role
The Egress team was formed to own a very important aspect of Cloudflare's network-as-a-service: Egress connectivity for our vast portfolio of products.
As a member of the Egress team, you will be a key technical contributor to a cutting edge network software infrastructure used by a growing number of Cloudflare products to connect to any destination from Cloudflare's network. Among the products powered by this technology is WARP, our Zero Trust platform, and Cloudflare's egress proxy for the
iCloud Private Relay
product.
You will work closely with various Engineering teams to translate their requirements into new capabilities on the platform. Likewise you will partner with Network Engineering and SRE to ensure that the technology makes the best use of Cloudflare's world-class edge network.
You will participate in all stages of the software development lifecycle, from designing and documenting systems, to writing code and automated tests, to planning, managing, and monitoring production software deployments. You will work with a wide range of technologies and programming languages, including Rust, Go, Linux networking, ClickHouse, PostgreSQL, Grafana, Kubernetes, and more.
Engineering teams at Cloudflare operate a Run What You Build model, including ours. We are responsible for the health of our system and participate in our team's on-call rotation as part of operational responsibilities.
Because you’ll be solving problems of massive scale and significance, you are a growth-oriented individual who enjoys being outside of your comfort zone. You are comfortable in a fast-paced but sensible work environment. You value curiosity and empathy and lead with these values.
Responsibilities
Make daily progress on assigned engineering tasks, such as designing systems, writing specs, writing and pushing code via git, writing release plans for complex changes, rolling out production releases and working with other teams to coordinate system actions
Participate in daily and ad-hoc team meetings to share progress and collaborate
Participate in the team’s on-call schedule to ensure uptime of our production systems
Investigating customer issues that are escalated to the Egress team for triage and resolution
Responding to questions from other teams in the Egress team’s internal, public chat room
Using AI-powered tools and systems as part of your daily workflow to analyze and extend codebases, introspect production systems and datasets, and accelerate problem-solving
Desirable Skills, Knowledge, and Experience
Programming experience in
Go or Rust
and a desire to improve your skills in both.
A solid grasp of networking protocols in Layers 3 and 4 of the OSI Model.
Knowledge of HTTP, TLS, and CDN networks.
Experience in implementing secure and highly-available distributed systems.
Strong ability to debug issues in complex systems.
Strong collaboration and communication skills.
Experience/interest in HTTP networking and VPN technologies.
Experience/interest in network performance monitoring and tuning.
Willingness to adopt and integrate AI tools and systems into your engineering workflow.
Bonus Points
Knowledge of TCP/IP and Internet routing.
Professional systems-level programming experience in Rust.
Experience building tools and APIs.
Experience with monitoring, alerting, and debugging large-scale distributed systems
Experience participating in an on-call rotation.
Experience using AI-assisted development tools (e.g., code completion, codebase analysis, log/data exploration) in a professional setting.
Compensation
● For Portugal based hires: Estimated annual salary is between €66,000 - €91,000.
The final offer will be inclusive of time exemption, in alignment with the applicable law and collective bargaining agreements.
Equity
This role is eligible to participate in Cloudflare’s equity plan.
What Makes Cloudflare Special?
We’re not just a highly ambitious, large-scale technology company. We’re a highly ambitious, large-scale technology company with a soul. Fundamental to our mission to help build a better Internet is protecting the free and open Internet.
Project Galileo
: Since 2014, we've equipped more than 2,400 journalism and civil society organizations in 111 countries with powerful tools to defend themselves against attacks that would otherwise censor their work, technology already used by Cloudflare’s enterprise customers--at no cost.
Athenian Project
: In 2017, we created the Athenian Project to ensure that state and local governments have the highest level of protection and reliability for free, so that their constituents have access to election information and voter registration. Since the project, we've provided services to more than 425 local government election websites in 33 states.
1.1.1.1
: We released
1.1.1.1
to help fix the foundation of the Internet by building a faster, more secure and privacy-centric public DNS resolver. This is available publicly for everyone to use - it is the first consumer-focused service Cloudflare has ever released. Here’s the deal - we don’t store client IP addresses never, ever. We will continue to abide by our
privacy commitment
and ensure that no user data is sold to advertisers or used to target consumers.
Sound like something you’d like to be a part of? We’d love to hear from you!
Please note that applicants who progress to the offer stage of the interview process may be asked to attend an in-person interview within one of the Cloudflare Offices or Cloudflare Hubs. More details about this will be available at that stage of the interview process.
This position may require access to information protected under U.S. export control laws, including the U.S. Export Administration Regulations. Please note that any offer of employment may be conditioned on your authorization to receive software or technology controlled under these U.S. export laws without sponsorship for an export license.
Cloudflare is proud to be an equal opportunity employer. We are committed to providing equal employment opportunity for all people and place great value in both diversity and inclusiveness. All qualified applicants will be considered for employment without regard to their, or any other person's, perceived or actual
race, color, religion, sex, gender, gender identity, gender expression, sexual orientation, national origin, ancestry, citizenship, age, physical or mental disability, medical condition, family care status, or any other basis protected by law.
We are an AA/Veterans/Disabled Employer.
Cloudflare provides reasonable accommodations to qualified individuals with disabilities. Please tell us if you require a reasonable accommodation to apply for a job. Examples of reasonable accommodations include, but are not limited to, changing the application process, providing documents in an alternate format, using a sign language interpreter, or using specialized equipment. If you require a reasonable accommodation to apply for a job, please contact us via e-mail at
hr@cloudflare.com
or via mail at 101 Townsend St. San Francisco, CA 94107.
Location: San Francisco, CA | New York City, NY | Seattle, WA
About Anthropic
Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
About the role:
We are looking for experienced software engineers to join our Platform organization. We build the foundational primitives that accelerate product development across Anthropic, and own infrastructure and systems that teams depend on to ship reliably and at scale - either internally or by hundreds of thousands of external global users and companies at all stages.
You'll independently scope complex, multi-month projects, drive cross-org alignment through ambiguous problem spaces, and make architectural decisions that shape how Anthropic builds and scales its products. You'll partner directly with research to productize cutting-edge capabilities, and will have lasting impact on the platform that hundreds of thousands of companies and internal/external engineers depend on every day.
We have multiple teams that are currently hiring. Team placement occurs after the interview process, taking into account your interests and experience alongside organizational needs. This flexible approach allows us to match talented engineers with the backend product efforts where they'll have the greatest impact and growth potential.
You might be a good fit if you:
Have a minimum of 8 years of practical experience as a backend or platform engineer building scalable distributed systems, ideally operating at tech lead level or equivalent
distributed systems, cloud-native products, developer tools, or external developer facing products
Have strong fundamentals in service-oriented architectures, networking, and systems design, ideally having owned both the technical vision and execution of a foundational platform system end to end
Are proficient in Python, Go, Rust, or similar systems languages
Have experience with cloud infrastructure (GCP, AWS, or Azure), container orchestration (Kubernetes), and/or multi-cloud networking
Take full ownership of your work—from design through deployment and operations
Can navigate ambiguity and make sound technical decisions independently, and have ideally operated in 0 to 1 and more mature team or company settings
Take a product-focused approach to platform work and care about building solutions that are robust, scalable, and easy to use
Care about building systems that other engineers love to use
Deadline to apply:
None. Applications will be reviewed on a rolling basis.
Location Preference:
Preference will be given to candidates based in NY, SEA, SF or the Bay Area given the current location of team.
The annual compensation range for this role is listed below.
For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.
Annual Salary:
$405,000
—
$485,000 USD
Logistics
Minimum education:
Bachelor’s degree or an equivalent combination of education, training, and/or experience
Required field of study:
A field relevant to the role as demonstrated through coursework, training, or professional experience
Minimum years of experience:
Years of experience required will correlate with the internal job level requirements for the position
Location-based hybrid policy:
Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
Visa sponsorship:
We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.
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: London
Perplexity is looking for a Search Machine Learning Engineer Intern to help build the next generation of advanced search technologies, with a focus on retrieval and ranking. You will work closely with experienced engineers to improve search quality, experiment with new models, and ship features that directly impact how users search and discover information.
Internship program: 12 - 24 weeks, full-time, in-person in the London office.
Responsibilities:
- Contribute to experiments that improve search quality through better models, data usage, and evaluation tools, under the guidance of senior engineers.
- Design and implement components of the search platform and model stack, including retrieval, ranking, and classification models.
- Train evaluating models (including LLM-based approaches) for retrieval, ranking, and classification tasks.
- Support deployment and monitoring of search and ranking models in a scalable and performant way.
- Help build and iterate on RAG pipelines for grounding and answer generation.
- Collaborate with Data, AI, Infrastructure and Product teams to deliver improvements quickly and learn best practices in production ML.
Qualifications:
- Strong foundation in machine learning and statistics, with coursework or projects related to information retrieval, ranking, or recommender systems.
- Experience with Python and common ML frameworks (e.g. PyTorch, TensorFlow, JAX) through academic, open source, or personal projects.
- Familiarity with evaluating model quality using offline metrics and/or A/B testing is a plus, but not required.
- Previous experience (internships, research, or significant projects) working on search, recommendation, or NLP is a plus, but not required.
- Self-driven and curious, with a strong sense of ownership, willingness to learn, and comfort working in a fast-paced environment
- Experience with Rust will be a plus
Location: London
Perplexity is looking for a Search Machine Learning Engineer Intern to help build the next generation of advanced search technologies, with a focus on retrieval and ranking. You will work closely with experienced engineers to improve search quality, experiment with new models, and ship features that directly impact how users search and discover information.
Internship program: 12 - 24 weeks, full-time, in-person in the London office.
Responsibilities:
- Contribute to experiments that improve search quality through better models, data usage, and evaluation tools, under the guidance of senior engineers.
- Design and implement components of the search platform and model stack, including retrieval, ranking, and classification models.
- Train evaluating models (including LLM-based approaches) for retrieval, ranking, and classification tasks.
- Support deployment and monitoring of search and ranking models in a scalable and performant way.
- Help build and iterate on RAG pipelines for grounding and answer generation.
- Collaborate with Data, AI, Infrastructure and Product teams to deliver improvements quickly and learn best practices in production ML.
Qualifications:
- Strong foundation in machine learning and statistics, with coursework or projects related to information retrieval, ranking, or recommender systems.
- Experience with Python and common ML frameworks (e.g. PyTorch, TensorFlow, JAX) through academic, open source, or personal projects.
- Familiarity with evaluating model quality using offline metrics and/or A/B testing is a plus, but not required.
- Previous experience (internships, research, or significant projects) working on search, recommendation, or NLP is a plus, but not required.
- Self-driven and curious, with a strong sense of ownership, willingness to learn, and comfort working in a fast-paced environment
- Experience with Rust will be a plus
Location: Switzerland
Workplace: Remote
About Inworld
Inworld is a research lab and inference provider focused on realtime AI for consumer-facing applications. We build first-party speech models, serve LLMs, and run the inference behind modular APIs designed for high-volume, realtime workloads.
Hundreds of millions of users interact with Inworld powered apps every day and we serve over 10 trillion LLM tokens per month. Our models and infrastructure support consumer applications across companions, healthcare, fitness, education, media, and more. Our work spans model research, realtime inference, large-scale serving infrastructure, and the APIs developers use to bring these capabilities into production.
We’ve raised more than $125M from Lightspeed Venture Partners, Section 32, Kleiner Perkins, Microsoft’s M12 venture fund, Founders Fund, Meta, Stanford, and others. Our technology has powered experiences from companies including NVIDIA, Microsoft Xbox, Niantic, Logitech Streamlabs, Wishroll, Little Umbrella, and Bible Chat. Inworld has also been recognized by CB Insights as one of the 100 most promising AI companies globally and named one of LinkedIn’s Top 10 Startups in the USA.
Who We're Looking For
A year ago, reliably working agentic systems and sub-second multimodal inference at scale barely existed. Nobody has a decade of experience here. So we're not screening for a resume template — we're looking for strong people from varied backgrounds who learn fast, thrive in ambiguity, and can show us what they've built, broken, and understood.
Experience We Find Useful
You don't need all of this. But you need enough to make a case.
- Inference Optimization. Deep understanding of modern serving frameworks and techniques like vLLM or TRT-LLM.
- Model Acceleration. Hands-on experience with quantization, distillation, caching strategies , continuous batching, paged attention, and speculative decoding.
- High-Performance Systems. Proficiency in C++, CUDA, Rust, or highly optimized Python. You know how to profile code and squeeze every ounce of performance out of NVIDIA GPUs.
- Distributed Systems & Scaling. Experience with Kubernetes, Ray, custom load balancing, multi-GPU/multi-node inference, and reliably handling thousands of concurrent connections.
- Public work. Non-trivial systems programming projects, open-source contributions to major inference engines, or deep-dive technical write-ups.
- Full-cycle ownership. You can take a model from the research team, containerize it, optimize its serving, and ensure it runs reliably in production.
- Background. PhD in CS, Physics, Math, or equivalent practical experience building backend or ML systems.
- Professional fluency in English (written and spoken) is required, as you will be collaborating daily with our US-based leadership and engineering teams.
Who Thrives Here
- You don’t need a roadmap to start walking; you’re comfortable picking a direction and building the map as you go.
- You believe engineering isn't finished until it’s shipped and stable. You have a bias for impact over purely theoretical optimizations.
- You don't just ship code; you obsess over the why. You’re the first to question an architecture if you think there’s a better way to solve the core latency or throughput problem.
- You aren't satisfied with "the PM said so." You thrive on deep context and want to understand the fundamental logic behind every decision we make.
What Working Here Is Like
We hand you unclear problems and expect you to make them clear. We value engineers who say "I don't know yet" and then design the benchmark or prototype that finds out. We treat performance, latency, and reliability as first-class product features, not a box to check before launch. Impact comes before everything else, though we support sharing work and open-source contributions that move the field forward. Your work should be visible. Flat structure, fast iterations, minimal process theater.
Location & Employment
- Location: remote within Switzerland
- Employment type: Full-time, permanent employment
- Hiring model: Employment via Employer of Record (EOR)
Candidates must already have the legal right to work in Switzerland, as visa sponsorship is not available for this role. For candidates interested in relocating to the San Francisco Bay Area in the future, full U.S. visa and relocation support may be available, subject to business needs and applicable legal and work authorization requirements.
Inworld Jobs Privacy https://inworld.ai/jobs-privacy
Location: UK
Workplace: Remote
About Inworld
Inworld is a research lab and inference provider focused on realtime AI for consumer-facing applications. We build first-party speech models, serve LLMs, and run the inference behind modular APIs designed for high-volume, realtime workloads.
Hundreds of millions of users interact with Inworld powered apps every day and we serve over 10 trillion LLM tokens per month. Our models and infrastructure support consumer applications across companions, healthcare, fitness, education, media, and more. Our work spans model research, realtime inference, large-scale serving infrastructure, and the APIs developers use to bring these capabilities into production.
We’ve raised more than $125M from Lightspeed Venture Partners, Section 32, Kleiner Perkins, Microsoft’s M12 venture fund, Founders Fund, Meta, Stanford, and others. Our technology has powered experiences from companies including NVIDIA, Microsoft Xbox, Niantic, Logitech Streamlabs, Wishroll, Little Umbrella, and Bible Chat. Inworld has also been recognized by CB Insights as one of the 100 most promising AI companies globally and named one of LinkedIn’s Top 10 Startups in the USA.
Who We're Looking For
A year ago, reliably working agentic systems and sub-second multimodal inference at scale barely existed. Nobody has a decade of experience here. So we're not screening for a resume template — we're looking for strong people from varied backgrounds who learn fast, thrive in ambiguity, and can show us what they've built, broken, and understood.
Experience We Find Useful
You don't need all of this. But you need enough to make a case.
- Inference Optimization. Deep understanding of modern serving frameworks and techniques like vLLM or TRT-LLM.
- Model Acceleration. Hands-on experience with quantization, distillation, caching strategies , continuous batching, paged attention, and speculative decoding.
- High-Performance Systems. Proficiency in C++, CUDA, Rust, or highly optimized Python. You know how to profile code and squeeze every ounce of performance out of NVIDIA GPUs.
- Distributed Systems & Scaling. Experience with Kubernetes, Ray, custom load balancing, multi-GPU/multi-node inference, and reliably handling thousands of concurrent connections.
- Public work. Non-trivial systems programming projects, open-source contributions to major inference engines, or deep-dive technical write-ups.
- Full-cycle ownership. You can take a model from the research team, containerize it, optimize its serving, and ensure it runs reliably in production.
- Background. PhD in CS, Physics, Math, or equivalent practical experience building backend or ML systems.
Who Thrives Here
- You don’t need a roadmap to start walking; you’re comfortable picking a direction and building the map as you go.
- You believe engineering isn't finished until it’s shipped and stable. You have a bias for impact over purely theoretical optimizations.
- You don't just ship code; you obsess over the why. You’re the first to question an architecture if you think there’s a better way to solve the core latency or throughput problem.
- You aren't satisfied with "the PM said so." You thrive on deep context and want to understand the fundamental logic behind every decision we make.
What Working Here Is Like
We hand you unclear problems and expect you to make them clear. We value engineers who say "I don't know yet" and then design the benchmark or prototype that finds out. We treat performance, latency, and reliability as first-class product features, not a box to check before launch. Impact comes before everything else, though we support sharing work and open-source contributions that move the field forward. Your work should be visible. Flat structure, fast iterations, minimal process theater.
The base salary range for this full-time position is £140,000 – £200,000. In addition to base pay, total compensation includes equity and benefits. Within the range, individual pay is determined by work location, level, and additional factors, including competencies, experience, and business needs. The base pay range is subject to change and may be modified in the future.
Candidates must already have the legal right to work in the United Kingdom, as visa sponsorship is not available for this role. For candidates interested in relocating to the San Francisco Bay Area in the future, full U.S. visa and relocation support may be available, subject to business needs and applicable legal and work authorization requirements.
Inworld Jobs Privacy https://inworld.ai/jobs-privacy
Location: Serbia
Workplace: Remote
About Inworld
Inworld is a research lab and inference provider focused on realtime AI for consumer-facing applications. We build first-party speech models, serve LLMs, and run the inference behind modular APIs designed for high-volume, realtime workloads.
Hundreds of millions of users interact with Inworld powered apps every day and we serve over 10 trillion LLM tokens per month. Our models and infrastructure support consumer applications across companions, healthcare, fitness, education, media, and more. Our work spans model research, realtime inference, large-scale serving infrastructure, and the APIs developers use to bring these capabilities into production.
We’ve raised more than $125M from Lightspeed Venture Partners, Section 32, Kleiner Perkins, Microsoft’s M12 venture fund, Founders Fund, Meta, Stanford, and others. Our technology has powered experiences from companies including NVIDIA, Microsoft Xbox, Niantic, Logitech Streamlabs, Wishroll, Little Umbrella, and Bible Chat. Inworld has also been recognized by CB Insights as one of the 100 most promising AI companies globally and named one of LinkedIn’s Top 10 Startups in the USA.
Who We're Looking For
A year ago, reliably working agentic systems and sub-second multimodal inference at scale barely existed. Nobody has a decade of experience here. So we're not screening for a resume template — we're looking for strong people from varied backgrounds who learn fast, thrive in ambiguity, and can show us what they've built, broken, and understood.
Experience We Find Useful
You don't need all of this. But you need enough to make a case.
- Inference Optimization. Deep understanding of modern serving frameworks and techniques like vLLM or TRT-LLM.
- Model Acceleration. Hands-on experience with quantization, distillation, caching strategies , continuous batching, paged attention, and speculative decoding.
- High-Performance Systems. Proficiency in C++, CUDA, Rust, or highly optimized Python. You know how to profile code and squeeze every ounce of performance out of NVIDIA GPUs.
- Distributed Systems & Scaling. Experience with Kubernetes, Ray, custom load balancing, multi-GPU/multi-node inference, and reliably handling thousands of concurrent connections.
- Public work. Non-trivial systems programming projects, open-source contributions to major inference engines, or deep-dive technical write-ups.
- Full-cycle ownership. You can take a model from the research team, containerize it, optimize its serving, and ensure it runs reliably in production.
- Background. PhD in CS, Physics, Math, or equivalent practical experience building backend or ML systems.
- Professional fluency in English (written and spoken) is required, as you will be collaborating daily with our US-based leadership and engineering teams.
Who Thrives Here
- You don’t need a roadmap to start walking; you’re comfortable picking a direction and building the map as you go.
- You believe engineering isn't finished until it’s shipped and stable. You have a bias for impact over purely theoretical optimizations.
- You don't just ship code; you obsess over the why. You’re the first to question an architecture if you think there’s a better way to solve the core latency or throughput problem.
- You aren't satisfied with "the PM said so." You thrive on deep context and want to understand the fundamental logic behind every decision we make.
What Working Here Is Like
We hand you unclear problems and expect you to make them clear. We value engineers who say "I don't know yet" and then design the benchmark or prototype that finds out. We treat performance, latency, and reliability as first-class product features, not a box to check before launch. Impact comes before everything else, though we support sharing work and open-source contributions that move the field forward. Your work should be visible. Flat structure, fast iterations, minimal process theater.
For candidates interested in relocating to the San Francisco Bay Area in the future, full U.S. visa and relocation support may be available, subject to business needs and applicable legal and work authorization requirements.
Inworld Jobs Privacy https://inworld.ai/jobs-privacy
Location: Germany
Workplace: Remote
About Inworld
Inworld is a research lab and inference provider focused on realtime AI for consumer-facing applications. We build first-party speech models, serve LLMs, and run the inference behind modular APIs designed for high-volume, realtime workloads.
Hundreds of millions of users interact with Inworld powered apps every day and we serve over 10 trillion LLM tokens per month. Our models and infrastructure support consumer applications across companions, healthcare, fitness, education, media, and more. Our work spans model research, realtime inference, large-scale serving infrastructure, and the APIs developers use to bring these capabilities into production.
We’ve raised more than $125M from Lightspeed Venture Partners, Section 32, Kleiner Perkins, Microsoft’s M12 venture fund, Founders Fund, Meta, Stanford, and others. Our technology has powered experiences from companies including NVIDIA, Microsoft Xbox, Niantic, Logitech Streamlabs, Wishroll, Little Umbrella, and Bible Chat. Inworld has also been recognized by CB Insights as one of the 100 most promising AI companies globally and named one of LinkedIn’s Top 10 Startups in the USA.
Who We're Looking For
A year ago, reliably working agentic systems and sub-second multimodal inference at scale barely existed. Nobody has a decade of experience here. So we're not screening for a resume template — we're looking for strong people from varied backgrounds who learn fast, thrive in ambiguity, and can show us what they've built, broken, and understood.
Experience We Find Useful
You don't need all of this. But you need enough to make a case.
- Inference Optimization. Deep understanding of modern serving frameworks and techniques like vLLM or TRT-LLM.
- Model Acceleration. Hands-on experience with quantization, distillation, caching strategies , continuous batching, paged attention, and speculative decoding.
- High-Performance Systems. Proficiency in C++, CUDA, Rust, or highly optimized Python. You know how to profile code and squeeze every ounce of performance out of NVIDIA GPUs.
- Distributed Systems & Scaling. Experience with Kubernetes, Ray, custom load balancing, multi-GPU/multi-node inference, and reliably handling thousands of concurrent connections.
- Public work. Non-trivial systems programming projects, open-source contributions to major inference engines, or deep-dive technical write-ups.
- Full-cycle ownership. You can take a model from the research team, containerize it, optimize its serving, and ensure it runs reliably in production.
- Background. PhD in CS, Physics, Math, or equivalent practical experience building backend or ML systems.
- Professional fluency in English (written and spoken) is required, as you will be collaborating daily with our US-based leadership and engineering teams.
Who Thrives Here
- You don’t need a roadmap to start walking; you’re comfortable picking a direction and building the map as you go.
- You believe engineering isn't finished until it’s shipped and stable. You have a bias for impact over purely theoretical optimizations.
- You don't just ship code; you obsess over the why. You’re the first to question an architecture if you think there’s a better way to solve the core latency or throughput problem.
- You aren't satisfied with "the PM said so." You thrive on deep context and want to understand the fundamental logic behind every decision we make.
What Working Here Is Like
We hand you unclear problems and expect you to make them clear. We value engineers who say "I don't know yet" and then design the benchmark or prototype that finds out. We treat performance, latency, and reliability as first-class product features, not a box to check before launch. Impact comes before everything else, though we support sharing work and open-source contributions that move the field forward. Your work should be visible. Flat structure, fast iterations, minimal process theater.
For candidates interested in relocating to the San Francisco Bay Area in the future, full U.S. visa and relocation support may be available, subject to business needs and applicable legal and work authorization requirements.
Inworld Jobs Privacy https://inworld.ai/jobs-privacy
Location: Mountain View, California, USA
Workplace: Hybrid
About Inworld
Inworld is a research lab and inference provider focused on realtime AI for consumer-facing applications. We build first-party speech models, serve LLMs, and run the inference behind modular APIs designed for high-volume, realtime workloads.
Hundreds of millions of users interact with Inworld powered apps every day and we serve over 10 trillion LLM tokens per month. Our models and infrastructure support consumer applications across companions, healthcare, fitness, education, media, and more. Our work spans model research, realtime inference, large-scale serving infrastructure, and the APIs developers use to bring these capabilities into production.
We’ve raised more than $125M from Lightspeed Venture Partners, Section 32, Kleiner Perkins, Microsoft’s M12 venture fund, Founders Fund, Meta, Stanford, and others. Our technology has powered experiences from companies including NVIDIA, Microsoft Xbox, Niantic, Logitech Streamlabs, Wishroll, Little Umbrella, and Bible Chat. Inworld has also been recognized by CB Insights as one of the 100 most promising AI companies globally and named one of LinkedIn’s Top 10 Startups in the USA.
Who We're Looking For
A year ago, reliably working agentic systems and sub-second multimodal inference at scale barely existed. Nobody has a decade of experience here. So we're not screening for a resume template — we're looking for strong people from varied backgrounds who learn fast, thrive in ambiguity, and can show us what they've built, broken, and understood.
Experience We Find Useful
You don't need all of this. But you need enough to make a case.
- Inference Optimization. Deep understanding of modern serving frameworks and techniques like vLLM or TRT-LLM.
- Model Acceleration. Hands-on experience with quantization, distillation, caching strategies , continuous batching, paged attention, and speculative decoding.
- High-Performance Systems. Proficiency in C++, CUDA, Rust, or highly optimized Python. You know how to profile code and squeeze every ounce of performance out of NVIDIA GPUs.
- Distributed Systems & Scaling. Experience with Kubernetes, Ray, custom load balancing, multi-GPU/multi-node inference, and reliably handling thousands of concurrent connections.
- Public work. Non-trivial systems programming projects, open-source contributions to major inference engines, or deep-dive technical write-ups.
- Full-cycle ownership. You can take a model from the research team, containerize it, optimize its serving, and ensure it runs reliably in production.
- Background. PhD in CS, Physics, Math, or equivalent practical experience building backend or ML systems.
Who Thrives Here
- You don’t need a roadmap to start walking; you’re comfortable picking a direction and building the map as you go.
- You believe engineering isn't finished until it’s shipped and stable. You have a bias for impact over purely theoretical optimizations.
- You don't just ship code; you obsess over the why. You’re the first to question an architecture if you think there’s a better way to solve the core latency or throughput problem.
- You aren't satisfied with "the PM said so." You thrive on deep context and want to understand the fundamental logic behind every decision we make.
What Working Here Is Like
We hand you unclear problems and expect you to make them clear. We value engineers who say "I don't know yet" and then design the benchmark or prototype that finds out. We treat performance, latency, and reliability as first-class product features, not a box to check before launch. Impact comes before everything else, though we support sharing work and open-source contributions that move the field forward. Your work should be visible. Flat structure, fast iterations, minimal process theater.
We believe in the power of in-person collaboration to solve the hardest problems and foster a strong team culture. We offer relocation assistance and look forward to you joining us in our Mountain View office.
The base salary range for this full-time position is $270,000 - $430,000+ bonus + equity + benefits.
Inworld Jobs Privacy https://inworld.ai/jobs-privacy
О ПРОЕКТЕ
Мы создаем точку входа в мир децентрализованных финансов и блокчейна через геймификацию. Это целая Web3-экосистема, где образование встречается с технологиями. Ищем лидера, который возглавит инженерную команду и построит масштабируемую архитектуру с нуля.
ВАШИ КЛЮЧЕВЫЕ ЗАДАЧИ
— Strategy: Формирование тех. стратегии и архитектуры всей Web3-экосистемы.
— Design: Проектирование интеграций (смарт-контракты, индексоры, off-chain сервисы, auth flows).
— Planning: Трансформация продуктового роадмапа в четкие инженерные планы.
— Leadership: Руководство тимлидами и разработчиками, менторство и масштабирование команды.
— Hands-on: Личное участие в разработке ключевых модулей, Code Review и принятие архитектурных решений.
— Safety: Обеспечение надежности и безопасного Delivery совместно с DevOps.
НАШ ТЕХСТЕК
— Blockchain: Solidity / EVM (Ethereum), Rust / Solana.
— Languages: TypeScript / Node.js, React / Next.js.
— Web3 Tools: ethers.js / web3.js, WalletConnect, MetaMask.
— Data: The Graph / Indexers, RPC providers.
— Infra: Docker, Kubernetes, CI/CD.
ПОЧЕМУ У НАС КРУТО?
— Impact: Реальное влияние на развитие глобальной экосистемы, а не локальной фичи.
— Autonomy: Высокая степень свободы в решениях и прямой доступ к C-level.
— Culture: Международный контекст, быстрые процессы и отсутствие бюрократии.
— Growth: Шанс выстроить инженерные практики «под себя» и собрать зрелую систему.
Location: United States, Remote
Are you on a mission to create games that bond players together into deeply invested communities? Would you like to work on something comedic with lighthearted and whimsical characters? Do you enjoy the challenge of solving complex, non-deterministic problems with simple, pragmatic solutions?
As a
Senior Online Game Services Engineering Lead
for teamLFG, you will design and build the core services that take our game from prototype to production, from gathering requirements to implementation and scaling. Most importantly, you will work with a fun, dedicated, and passionate cross-discipline team devoted to turning a creative dream into a new franchise at PlayStation.
We look for candidates who value joining a mission-focused team with a diverse set of backgrounds, experiences, interests, and viewpoints.
RESPONSIBILITIES
Build and maintain production-quality backend game services
Directly manage and lead several engineers, providing support, technical mentorship, constructive feedback, and career development coaching
Collaborate daily with a cross-disciplinary team to design and build the services-driven features for the game (including sessions, matchmaking, and progression)
Effectively write clear, concise, and accurate technical documentation and email communication, with the ability to summarize complex topics and achieve alignment across multiple teams
Calmly deal with production incidents, participating in an on-call rotation after launch
REQUIRED SKILLS
Production experience designing and building backend game services
Ability to communicate with customers of different technical and non-technical backgrounds to help them navigate technology and achieve their goals
Experience leading (direct management, mentoring, or guiding) engineers
Ability to diagnose complex system failures using logging and metrics
Ability to write, debug, and maintain code in Rust, C++, or C#
Embrace a “we” culture in addition to being an outstanding collaborator and communicator
NICE-TO-HAVE SKILLS
Experience with “games as a service” online game development
Working knowledge of Docker, Git, and Linux (including shell scripting and system configuration)
At SIE, we consider several factors when setting each role’s base pay range, including the competitive benchmarking data for the market and geographic location.
Please note that the base pay range may vary in line with our hybrid working policy and individual base pay will be determined based on job-related factors which may include knowledge, skills, experience, and location.
In addition, this role is eligible for SIE’s top-tier benefits package that includes medical, dental, vision, matching 401(k), paid time off, wellness program and coveted employee discounts for Sony products. This role also may be eligible for a bonus package. Click
here
to learn more.
This is a flexible role that can be remote, with varying pay ranges based on geographic location. For example, if you are based out of Seattle, the estimated base pay range for this role is listed below.
$217,000
—
$325,000 USD
Please note, Sony Interactive Entertainment conducts background checks at the offer stage for all new employees (which may include criminal background checks for some roles) and will need to process personal information to support these checks.
Please refer to our
Candidate Privacy Notice
for more information about what personal information we collect, how we use it, who we share it with, and your data protection rights.
San Francisco; Palo Alto; New York City
San Francisco; Palo Alto; New York City
Не хотите искать каждый день?
Настройте подбор один раз, и подходящие вакансии будут сами приходить в Telegram.
Настройте подбор, и подходящие вакансии будут приходить в Telegram.
Japan; Remote; Singapore; Location / Timezone; Remote-friendly for exceptional candidates — must have 3+ hours overlap with Tokyo business hours (JST, UTC+9)
seniorHanoi OR Ho Chi Minh City
seniorГеография найма не уточнена
San Francisco, CA | New York City, NY | Seattle, WA; Preference will be given to candidates based in NY, SEA, SF or the Bay Area given the current location of team
УдалённоseniorПротиворечивые условия: For candidates interested in relocating to the San Francisco Bay Area in the future, full U.S; visa and relocation support may be available, subject to business needs and applicable legal and work authorization requirements
УдалённоseniorПротиворечивые условия: For candidates interested in relocating to the San Francisco Bay Area in the future, full U.S; visa and relocation support may be available, subject to business needs and applicable legal and work authorization requirements
УдалённоleadПротиворечивые условия: For candidates interested in relocating to the San Francisco Bay Area in the future, full U.S; visa and relocation support may be available, subject to business needs and applicable legal and work authorization requirements
УдалённоleadПротиворечивые условия: For candidates interested in relocating to the San Francisco Bay Area in the future, full U.S; visa and relocation support may be available, subject to business needs and applicable legal and work authorization requirements
ГибридseniorMountain View, California, USA; We offer relocation assistance and look forward to you joining us in our Mountain View office
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