Research Engineer, Machine Learning (RL Velocity)

• Own the efficiency and reliability of Anthropic's RL Science stack — the infrastructure, tooling, and systems that let researchers iterate quickly on training runs. • Build and improve core RL training infrastructure, identifying and removing bottlenecks across the stack through debugging, profiling, and rearchitecting where needed. • Partner closely with researchers and adjacent engineering teams (inference, sandboxing, and others) to understand pain points and ship tooling that makes them faster. • Own the reliability and performance of research runs end to end, contributing to design decisions that shape how Anthropic does RL at scale. • Be based at least 25% of the time in Anthropic's London office.

• Have strong software engineering fundamentals with 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. • Be comfortable operating across the stack — from low-level performance work to RL algorithms. • Have a bias toward shipping and iterating quickly with high agency and low ego.

• Have experience with large-scale distributed training including RL, pre-training, or post-training. • Have familiarity with JAX, PyTorch, or similar ML frameworks. • Have a track record of operating at the edge of research and infrastructure in a fast-moving environment.

• Build and improve the RL training infrastructure researchers depend on day to day. • Identify and remove bottlenecks across the RL stack through debugging, profiling, and rearchitecting. • Partner with researchers and adjacent engineering teams to ship tooling that accelerates their work. • Own reliability and performance of research runs end to end. • Contribute to design decisions shaping how Anthropic does RL at scale.

• Earn £370,000–£630,000 per year. • Receive visa sponsorship — Anthropic retains an immigration lawyer and makes every reasonable effort to support visa applications. • Access optional equity donation matching, generous vacation and parental leave, and flexible working hours. • Work on high-leverage infrastructure where small improvements compound across every researcher and every training run at one of the world's leading AI research organisations.

Anthropic is a public benefit corporation headquartered in San Francisco, with a mission to create reliable, interpretable, and steerable AI systems. The RL Velocity team owns the efficiency and reliability of Anthropic's RL Science stack, building the infrastructure and tooling that enable researchers to iterate quickly on training runs and ship better models faster. The London team is part of Anthropic's broader RL engineering effort.