Research Engineer within Reinforcement Learning at Anthropic, collaborating with researchers and engineers to advance the capabilities and safety of large language models. This role blends research and engineering responsibilities, requiring implementation of novel approaches and contribution to research direction across foundational reinforcement learning projects.
Research Engineer, Machine Learning (Reinforcement Learning) en Anthropic
Híbrido - London, UK
Más vacantes en AnthropicSalary
GBP 260,000 - 630,000
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
- enjoys pair programming
- care about 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 capabilities and safety of large language models
- implement novel approaches and contribute to research direction
- conduct fundamental research 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
- 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 training and evaluation workflows
- collaborate across research and engineering teams to develop automated testing frameworks, design clean APIs, and build scalable infrastructure
Technologies
Pythonasync/concurrent programmingTrioPyTorchTensorFlowJAXLLM architecturesreinforcement learningvirtualizationsandboxed code execution environmentsKubernetesdistributed systemshigh-performance computingRustC++
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