Research Engineer, Machine Learning (Reinforcement Learning)

Overview In this role you will co-develop reinforcement learning capabilities for large language models, balancing research exploration with robust engineering. You'll help build scalable RL infrastructure, create test environments, and push model reasoning and tool-use capabilities.

You work closely with researchers and engineers to advance safe, impactful AI systems at scale. This position offers opportunities to shape next-gen models and contribute to Anthropic's mission of trustworthy AI. Pay / Benefits Competitive compensation and benefits Optional equity donation matching Generous vacation and parental leave Flexible working hours Lovely office space for collaboration Responsibilities Architect and optimize core RL infrastructure, including training abstractions and distributed experiment management across GPU clusters Scale systems to support increasingly complex research workflows Design and implement novel training environments and evaluation methodologies for RL agents Improve stack performance via profiling, optimization, and benchmarking Develop efficient caching and debugging for distributed training and evaluation Collaborate across research and engineering to build automated testing frameworks and scalable APIs Key requirements Proficient in Python and async/concurrent programming (e.g., Trio) Experience with ML frameworks (PyTorch, TensorFlow, JAX) Industry experience in machine learning research Ability to balance research exploration with engineering implementation Enjoys pair programming and collaborative work Strong code quality, testing, and performance mindset Solid systems design and communication skills Passion for AI safety and beneficial AI Collaboration Communication Problem-solving Python async/concurrent programming (Trio) PyTorch

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