About the RL Teams
Our Reinforcement Learning teams play 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 our latest Claude models. Our work spans several key areas:
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Developing systems that enable models to use computers effectively
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Advancing code generation through reinforcement learning
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Pioneering fundamental RL research for large language models
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Building scalable RL infrastructure and training methodologies
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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
We're hiring for the Code RL team within the RL organization. As a Research Engineer, you'll advance our models' ability to write, edit, test, debug, and ship real software — end to end, on real codebases, with real tools — and to do it correctly, fast, and safely.
This role blends research and engineering. You'll design RL environments and coding tasks, build the reward signals and verifiers that capture what "good code" means, run training experiments on frontier models, diagnose why a model does (or doesn't) get better at a class of software-engineering work, and improve the speed and reliability of the pipelines that make all of that iterate fast. Code RL spans several focus areas — from agentic coding behaviors and code correctness, to long-horizon autonomous engineering, to high-performance code for accelerators — and we'll match you to the area where you'll have the most impact.
You may be a good fit if you:
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Have strong software-engineering skills and deep Python expertise, including async/concurrent programming
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Are comfortable owning systems end to end and debugging across the stack
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Can balance research exploration with engineering implementation, and engage rigorously in shaping experimental design and interpreting results
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Care about code quality, testing, and performance
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Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems
Strong candidates may also have:
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Experience with reinforcement learning, RLHF, post-training, or LLM finetuning
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Built coding agents, code-execution sandboxes, eval harnesses, verifiers, or developer tooling
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Background in program analysis, testing, verification, compilers, or formal methods
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Experience with PyTorch and large-scale distributed training; performance profiling and optimization of ML systems
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CUDA / GPU or TPU kernel experience and accelerator-performance intuition
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Experience with virtualization and sandboxed code execution environments
Related roles
If your background leans toward one of these areas specifically, you may also want to look at these postings:
