Research, RL Scaling

Job summary

Responsibilities

Our team scales reinforcement learning for frontier models. Progress in RL is increasingly set by how well it scales: more rollouts, larger models, and training loops that keep large fleets of accelerators doing useful work.

• Co-design the RL recipe and the systems that run it: make recipe-level choices jointly with systems-level ones and validate them at frontier scale.
• Advance asynchronous RL algorithms.
• Improve the efficiency of rollout generation and its integration with training, treating inference as a first-class part of the RL loop.
• Run frontier-scale RL end to end: bring up new models and training setups, keep large runs stable and healthy.
• Jointly optimize the compute and training efficiency of RL: accelerator utilization, memory, communication, and low-precision numerics.

What they're looking for

• Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX).
• Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding.
• Clarity in communication, an ability to explain complex technical concepts in writing.
• Strong research judgment: clean ablations, honest baselines, and clear technical writing.
• PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding; or, equivalent industry research…

Tech stack

• Python
• PyTorch
• TensorFlow
• JAX
• vLLM