As a Research Engineer on the RL Velocity 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.
You'll 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.
Strong software engineering fundamentals and a track record of building performant, reliable systems.
Experience with ML infrastructure, distributed systems, or research tooling.
Comfortable operating across the stack, from low-level performance work to RL algorithms.
A bias toward shipping and iterating quickly, with a mix of high agency and low ego.
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.
XML job scraping automation by YubHub
