Research Engineer (Machine Learning, Reinforcement Learning Velocity) in London

Requirements

• 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
• (Desirable) Experience with large-scale distributed training (RL, pre-training, or post-training)
• (Desirable) Familiarity with JAX, PyTorch, or similar ML frameworks
• (Desirable) A track record of operating at the edge of research and infra in a fast-moving environment
• 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

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.

What the job involves

• 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.
• 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.