Research Engineer, Machine Learning (RL Velocity)

• We build and improve the RL training infrastructure that researchers depend on day to day.
• We identify and remove bottlenecks across the RL stack, including debugging, profiling, and rearchitecting where needed.
• We partner closely with researchers and adjacent engineering teams, including inference, sandboxing, and others, to understand pain points and ship tooling that makes them faster.
• We own the reliability and performance of research runs end to end.
• We contribute to design decisions that shape how we do RL at scale.

• We expect a Bachelors degree or an equivalent combination of education, training, and/or experience.
• We look for a field relevant to the role, demonstrated through coursework, training, or professional experience.
• We expect years of experience to align with the internal job level requirements for the position.
• You should have strong software engineering fundamentals and a track record of building performant, reliable systems.
• You should have experience with ML infrastructure, distributed systems, or research tooling.
• You should care about enabling other peoples work and find leverage through platforms rather than individual experiments.
• You should be comfortable operating across the stack, from low-level performance work to RL algorithms.
• You should have a bias toward shipping and iterating quickly, with high agency and low ego.
• Strong candidates may also have experience with large-scale distributed training, such as RL, pre-training, or post-training.
• Strong candidates may also have familiarity with JAX, PyTorch, or similar ML frameworks.
• Strong candidates may also have a track record of operating at the edge of research and infrastructure in a fast-moving environment.

We are Anthropic, a public benefit corporation headquartered in San Francisco, and our mission is to create reliable, interpretable, and steerable AI systems that are safe and beneficial for our users and for society. Our team is a fast-growing group of researchers, engineers, policy experts, and business leaders working together on high-impact AI research. The RL Velocity team focuses on the efficiency and reliability of our RL Science stack, building the infrastructure and tooling that help researchers iterate quickly and ship better models faster. We offer competitive compensation, optional equity donation matching, generous vacation and parental leave, flexible working hours, a lovely office space to collaborate, and a location-based hybrid policy that currently expects staff in one of our offices at least 25% of the time. We also sponsor visas where possible and encourage candidates to apply even if they do not meet every qualification.