Research Engineer / Research Scientist, RL Frontiers

Job summary

Responsibilities

Reinforcement learning is how Claude learns to reason, write code, and act autonomously over long horizons. The RL Scaling team works on how RL scales: what happens to throughput, stability, and learning efficiency as models get larger, episodes get longer, and compute grows by orders of…

• Study how RL training and sampling scale with model size, context length, and compute, and find the algorithmic and systems changes that keep scaling efficient
• Develop next-generation model architectures and RL algorithms, and make them run efficiently at frontier scale
• Take promising small-scale results to frontier-scale runs, and diagnose why they behave differently when they get there, whether the cause is numerical, algorithmic, or systemic
• Build the experimental infrastructure that sets research velocity: fast, reproducible comparisons of architecture and algorithm variants at meaningful scale
• Own end-to-end performance of our largest RL runs, from research code down to the hardware

What they're looking for

• Deep familiarity with modern transformer language models, including their architecture, training dynamics, and the behavior of large-scale optimization
• Hands-on experience training large models in a distributed setting, including the tradeoffs between data, tensor, and pipeline parallelism
• A track record of original technical work in ML training or systems, such as new methods, architectures, or optimizations, demonstrated through research, open-source, or…
• Ability to design rigorous experiments at scale, including baselines, ablations, and enough statistical care to trust a result that costs real compute
• Ability to reason quantitatively about the compute, memory, and communication costs of a model or algorithm

Tech stack

• Python
• JAX
• PyTorch
• C++
• Rust