Research Scientist, Scaling RL

Menlo Park · USD 250,000-350,000 · 5+ years experience · Full-time · Onsite · Posted Oct 1, 2026

At a glance

We're an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs across materials, energy, and beyond. Backed by world-class investors and growing rapidly, we operate at the pace the frontier requires. Our team brings deep expertise, genuine ownership, and a drive to push the boundaries of what's scientifically possible.

We're training frontier models to develop deep scientific knowledge and reasoning for scientific tasks.
You’ll study how RL scales with training compute, develop better algorithms, and take ideas from controlled experiments to our largest runs like Periodic Neon.

Design experiments to understand how RL performance scales with compute, model size, data, and reward quality, building on work such as ScaleRL
Develop better RL algorithms, spanning policy optimization, advantage estimation, exploration, and credit assignment for long-horizon RL tasks
Build adaptive sampling and curriculum methods that adjust task difficulty, problem selection, and the number of rollouts as models improve
Study bias and stability during RL training, including importance-sampling corrections and methods to tackle policy staleness and training–inference mismatch
Improve compute efficiency across training and inference through experiments with hyperparameters, such as length penalties, rollout counts, batch sizes, and update schedules

Hands-on experience training LLMs with reinforcement learning
Strong attention to detail and rigorous approach to answer questions scientifically
Coming up with small-scale RL setups that transfers to large-scale training runs
Comfort working across a complex training stack to implement, debug, and test new research ideas
Bachelor’s degree or similar experience