Anthropic is seeking a Research Engineer in Reinforcement Learning to work on both research and engineering tasks that improve large language models. The role blends advanced research in reinforcement learning with hands‑on engineering, including building scalable infrastructure, designing novel training environments, and optimizing performance across distributed GPU clusters. The position is based in San Francisco, CA, and operates under a hybrid model with on‑site requirements of at least 25% of the time.
Research Engineer, Machine Learning (Reinforcement Learning) en Anthropic
Híbrido - San Francisco, CA, United States
Más vacantes en AnthropicSalary
USD 500,000 - 850,000
Requirements
Skills
- Proficient in Python and async/concurrent programming with frameworks like Trio
- Experience with machine learning frameworks such as PyTorch, TensorFlow, and JAX
- Industry experience in machine learning research
- Ability to balance research exploration with engineering implementation
- Enjoys pair programming
- Strong focus on code quality, testing, and performance
- Strong systems design and communication skills
- Passionate about the potential impact of AI and committed to developing safe and beneficial systems
- Familiarity with LLM architectures and training methodologies
- Experience with reinforcement learning techniques and environments
- Experience with virtualization and sandboxed code execution environments
- Experience with Kubernetes
- Experience with distributed systems or high-performance computing
- Experience with Rust and/or C++
Responsibilities
- Collaborate with researchers and engineers to advance the capabilities and safety of large language models
- Implement novel approaches and contribute to the research direction in reinforcement learning
- Create agentic models via tool use for open-ended tasks such as computer use and autonomous software generation
- Improve reasoning abilities in areas such as mathematics and develop prototypes for internal use, productivity, and evaluation
- Architect and optimize core reinforcement learning infrastructure, including clean training abstractions and distributed experiment management across GPU clusters
- Design, implement, and test novel training environments, evaluations, and methodologies for reinforcement learning agents
- Drive performance improvements across the 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
Technologies
PythonTrioPyTorchTensorFlowJAXReinforcement learning frameworksLLM architecturesKubernetesRustC++Distributed systemsHigh-performance computingVirtualization and sandboxed code execution environmentsAsync/concurrent programming
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