Anthropic· AI Research & Engineering· Remote-Friendly (Travel-Required) | San Francisco, CA | New York City, NY
Research Engineer, Machine Learning (RL Velocity)
Classified Tasks (13)
Automate 0%Augment 54%Human-Only 46%
Augment (7)
AI assists, human decides
1. Build and improve the RL training infrastructure researchers depend on day-to-day
technical
2. Build and improve the core RL platform that underpins how Anthropic does RL
technical
3. Identify bottlenecks across the RL stack
analytical
4. Remove bottlenecks across the RL stack
technical
5. Debug components of the RL stack
technical
6. Profile performance across the RL stack
analytical
9. Ship tooling that increases researcher velocity
technical
Human-Only (6)
Requires human judgment
7. Rearchitect RL systems where needed to improve performance or reliability
technical
8. Partner closely with researchers and adjacent engineering teams (inference, sandboxing, etc.) to understand pain points
communication
10. Own the reliability of research runs end-to-end
operational
11. Own the performance of research runs end-to-end
operational
12. Contribute to design decisions that shape how Anthropic does RL at scale
leadership
13. Facilitate the broader organization’s ability to ship models faster by removing research bottlenecks
leadership
Job description
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