Research Engineer, Machine Learning (Reinforcement Learning)
London, UKFull-TimeMid-levelAI / Data Science
About Anthropic
- Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
About the teams
- Our Reinforcement Learning teams lead 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 Sonnet 4.5 and Opus 4.5. 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 are dedicated to implement our research at scale. Our Reinforcement Learning teams sit 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.
About the Role
- As a Research Engineer within Reinforcement Learning, 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.
Representative projects
- 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.
You may be a good fit if you
- 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
Strong candidates may have
- 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++
Strong candidates need not have
- Formal certifications or education credentials
- Academic research experience or publication history
Deadline to apply: None. Applications will be reviewed on a rolling basis.
- The annual compensation range for this role is listed below.
- For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.
How we're different
- We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.
- The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
