Research Engineer (Machine Learning, Reinforcement Learning)

Join Anthropic, a leading AI research and development company, as a Research Engineer specializing in Machine Learning and Reinforcement Learning. In this role, you will collaborate with a diverse team of researchers and engineers to advance the capabilities and safety of large language models. Your responsibilities will include implementing novel approaches, contributing to research direction, optimizing core reinforcement learning infrastructure, designing and testing training environments, and driving performance improvements across the stack. You will also have access to comprehensive benefits, including health insurance, paid parental leave, flexible paid time off, and more.

• Collaborate with a diverse group of researchers and engineers to advance the capabilities and safety of large language models, blending research and engineering responsibilities.
• Architect and optimize core reinforcement learning infrastructure, from clean training abstractions to distributed experiment management across GPU clusters.
• Design, implement, and test novel training environments, evaluations, and methodologies for reinforcement learning agents which push the state of the art for the next generation of models.
• We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed
• We urge you not to exclude yourself prematurely and to submit an application if you're interested in this work
• Have industry experience in machine learning research
• Care about code quality, testing, and performance
• Have strong systems design and communication skills
• Can balance research exploration with engineering implementation
• Are proficient in Python and async/concurrent programming with frameworks like Trio
• Enjoy pair programming (we love to pair!)
• Have experience with machine learning frameworks (PyTorch, TensorFlow, JAX)
• Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems
• Experience with reinforcement learning techniques and environments
• Experience with Kubernetes
• Academic research experience or publication history
• Formal certifications or education credentials
• Familiarity with LLM architectures and training methodologies
• Experience with distributed systems or high-performance computing
• Experience with Rust and/or C++
• Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience
• Experience with virtualization and sandboxed code execution environments