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How AI is used in this kind of work

Among computer and information research scientists, AI is already used for a large share of the tasks. Measured exposure for computer and information research scientists: 34% (high).

Share of this job’s tasks that AI is already used for at work, from real usage. It is not a forecast of job loss, and a low number means no use seen yet. Source: Anthropic Economic Index.

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About the job

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

- Compensation: $250,000–$350,000 + equity

- Visa sponsorship: Yes, we sponsor visas and will do everything we can to assist in this process.