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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