The opportunity
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…
What you'll do
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
Build performance and cost models for proposed architecture and algorithm: changes, and use them to decide which ideas get scaled
What they're looking for
- Research experience in reinforcement learning, optimization, or large-scale training, published or otherwise
- Experience developing RL algorithms for language models
- Experience with scaling laws or other quantitative models of training efficiency
- Experience designing or modifying transformer architectures beyond standard configurations
- Experience scaling training to large fleets of accelerators and debugging the: problems that only appear at scale
- Deep understanding of numerics in large-scale training, including: low-precision formats and sources of instability
- Familiarity with how GPU or TPU performance characteristics shape architecture and algorithm choices
- Experience with C++ or Rust
