Research Engineer, Machine Learning (RL Velocity)

Requirements

• 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

Nice to Haves

• 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

What You'll Be Doing

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

Perks and Benefits

• Bachelor’s degree or an equivalent combination of education, training, and/or experience
• Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
• Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position
• Location-based hybrid policy: Currently, at least 25% office presence expected
• Visa sponsorship is available
• We encourage you to apply even if you do not believe you meet every single qualification