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.
Research Engineer, Machine Learning (RL Velocity) at Anthropic
Remote - Remote (Travel-Required), San Francisco, CA, New York City, NY, USA
More jobs at AnthropicSalary
USD 500,000 - 850,000
Requirements
Skills
- Strong software engineering fundamentals and a track record of building performant, reliable systems
- Experience on ML infrastructure, distributed systems, or research tooling
- Care about enabling other people's work and finding leverage through platforms
- Comfortable operating across the stack, from low-level performance work to RL algorithms
- Bias toward shipping and iterating quickly, with a mix of high agency and low ego
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
Technologies
JAXPyTorchML frameworksDistributed systemsRL infrastructure
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