Research Engineer, Anthropic

• Are proficient in Python and async/concurrent programming with frameworks like Trio
• Have experience with machine learning frameworks (PyTorch, TensorFlow, JAX)
• Have industry experience in machine learning research
• Can balance research exploration with engineering implementation
• Enjoy pair programming (we love to pair!)
• Care about code quality, testing, and performance
• Have strong systems design and communication skills
• Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems
• Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience

Desirable

• The Horizons team leads Anthropic's reinforcement learning research and development, playing a critical role in advancing our AI systems
• We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of Claude 3.5 and 3.7 Sonnet. Our work spans several key areas:
• Developing systems that enable models to use computers effectively
• Advancing code generation through reinforcement learning
• Pioneering fundamental RL research for large language models
• Building scalable RL infrastructure and training methodologies
• Enhancing model reasoning capabilities
• We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe
• We partner with the applied production training team to bring research innovations into deployed models, and work hand-in-hand with dedicated RL engineering teams to implement our research at scale
• The Horizons team sits at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish
• As a Research Engineer on the Horizons team, you will collaborate with a diverse group of researchers and engineers to advance the capabilities and safety of large language models
• This role blends research and engineering responsibilities, requiring you to both implement novel approaches and contribute to the research direction
• You'll work on fundamental research in reinforcement learning, creating 'agentic' models via tool use for open-ended tasks such as computer use and autonomous software generation, improving reasoning abilities in areas such as mathematics, and developing prototypes for internal use, productivity, and evaluation
• Architect and optimize core reinforcement learning infrastructure, from clean training abstractions to distributed experiment management across GPU clusters. Help scale our systems to handle increasingly complex research workflows
• 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
• Drive performance improvements across our stack through profiling, optimization, and benchmarking. Implement efficient caching solutions and debug distributed systems to accelerate both training and evaluation workflows
• Collaborate across research and engineering teams to develop automated testing frameworks, design clean APIs, and build scalable infrastructure that accelerates AI research