Research Scientist

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.

 

About the Role

We're training frontier models to develop deep scientific knowledge and reasoning for scientific discovery. As a Midtraining Research Engineer, you'll take base models and improve their scientific reasoning: curating and generating data, building evals, and running large-scale training experiments. Your work will also lay the groundwork for our pre-training efforts down the line.

 

What You'll Do

• Identify, process, and curate novel sources of scientific data for large-scale model training.

• Generate high-quality synthetic data to fill gaps in scientific knowledge and reasoning.

• Build evaluations that correlate with downstream scientific task performance, working closely with RL researchers, physicists, and chemists.

• Develop and apply techniques such as self-distillation and on-policy distillation to improve model capability.

• Design and run large-scale training experiments, partnering with supercompute engineers to scale efficiently across thousands of GPUs.

• Build tools for yourself and the team to investigate how data choices shape model intelligence.

   

You Will Thrive in This Role If You Have

• Experience training LLMs on curated mixes of trillions of tokens.

• Experience on a dedicated evals team supporting a large production training run.

• Hands-on use of self-distillation, on-policy distillation, or similar methods in a real training pipeline.

• Experience with scaling laws and compute-optimal hyperparameters.

• Comfort working across data, evals, and training infrastructure.

   

Especially Strong Candidates May Also Have

• Experience optimizing throughput and reliability for large-scale distributed training runs.

• A background in AI for science or training on specialized domain data (e.g., protein, materials, or other scientific datasets).

• Experience creating evals or synthetic data for non verifiable tasks and tracking performance over live runs.