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.
Research Engineer, Machine Learning (RL Velocity) at Anthropic
Hybrid - London, UK
More jobs at AnthropicSalary
GBP 370,000 - 630,000
Requirements
Skills
- Strong software engineering fundamentals and a track record of building performant, reliable systems
- 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
- 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
- Experience with large-scale distributed training (RL, pre-training, or post-training)
- Familiarity with JAX, PyTorch, or similar ML frameworks
- Track record of operating at the edge of research and infra in a fast‑moving environment
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 adjacent engineering teams 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
JAXPyTorchRL training infrastructureDistributed systems
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