Research Engineer – Machine Learning (RL Velocity)

About the role

The RL Velocity team owns the efficiency and reliability of Anthropic’s RL Science stack. As a Research Engineer you will build and improve the core platform that powers RL training, removing bottlenecks and enabling faster model development across the organization.

Key responsibilities

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

Required profile

• Strong software engineering fundamentals with a track record of building performant, reliable systems.
• Experience working on ML infrastructure, distributed systems, or research tooling.
• Passion for enabling others’ work through platform‑level impact rather than individual experiments.
• Comfort operating across the stack, from low‑level performance work to RL algorithms.
• Bias toward shipping quickly, high agency, and low ego.

Required skills

• Experience with large‑scale distributed training (RL, pre‑training, or post‑training).
• Familiarity with JAX.
• Familiarity with PyTorch.

What we offer

• Competitive compensation ($500k–$850k USD annual) with optional equity donation matching.
• Generous vacation and parental leave, flexible working hours.
• Remote‑friendly hybrid work model with access to a collaborative office space in San Francisco.