Research Engineer, Machine Learning (Reinforcement Learning) at Anthropic

Hybrid - San Francisco, CA | New York City, NY

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The Research Engineer role at Anthropic focuses on advancing reinforcement learning research and engineering for large language models. The position involves building scalable RL infrastructure, designing training environments, improving performance through profiling and optimization, and collaborating across teams to implement safe and effective AI systems.

Salary

USD 500,000 - 850,000

Requirements

Skills

  • Proficient in Python and async/concurrent programming with frameworks like Trio
  • Experience with machine learning frameworks (PyTorch, TensorFlow, JAX)
  • Industry experience in machine learning research
  • Ability to balance research exploration with engineering implementation
  • Enjoyment of pair programming
  • Strong focus on code quality, testing, and performance
  • Strong systems design and communication skills
  • Passion for the potential impact of AI and commitment to 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

  • 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 to push the state of the art for the next generation of models
  • Drive performance improvements across our stack through profiling, optimization, and benchmarking, including implementing efficient caching solutions and debugging 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

PythonTrioPyTorchTensorFlowJAXKubernetesRustC++VirtualizationSandboxed code execution

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