Anthropic
Technology
ResearchEngineer,CodeRL(ReinforcementLearning)
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Research Engineer, Code RL (Reinforcement Learning) at Anthropic. Skills: Reinforcement Learning, Large language models, Software engineering, AI systems. Advance models' ability to write software. Advance models' ability to edit software”
Industry & Context.
Debugging across the stack; Diagnose model performance
What They're Looking For.
Must Have
Software-engineering skills, Deep Python expertise, Async/concurrent programming, Owning systems end to end, Debugging across the stack, Balancing research and engineering, Shaping experimental design, Interpreting experimental results, Caring about code quality, Caring about testing, Caring about performance, Commitment to safe systems, Commitment to beneficial systems, Bachelor's degree or equivalent experience
Nice to Have
Experience with reinforcement learning, Experience with RLHF, Experience with post-training, Experience with LLM finetuning, Built coding agents, Built code-execution sandboxes, Built eval harnesses, Built verifiers, Built developer tooling, Background in program analysis, Background in testing, Background in verification, Background in compilers, Background in formal methods, Experience with PyTorch, Large-scale distributed performance profiling, Optimization of ML systems, CUDA / GPU kernel experience, TPU kernel experience, Accelerator-performance intuition, Experience with virtualization, Experience with sandboxed code execution
What You'll Do.
Advance models' ability to write software
Advance models' ability to edit software
Advance models' ability to test software
Advance models' ability to debug software
Advance models' ability to ship software
Design RL environments
Run training experiments
Diagnose model performance
Improve pipeline speed
Improve pipeline reliability
How You'll Work.
Team & Collaboration
Alignment teams; Frontier red teams; Applied production training team; Research teams; Engineering teams
Full Job Description
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 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: 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 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
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