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.
Mechanics
Minimum education: Bachelor's degree or similar experience
Location: Menlo Park, CA (Soon: San Francisco, too)
Compensation: $250,000–$350,000 + equity
Visa sponsorship: Yes, we sponsor visas and will do everything we can to assist in this process.