- Salary
- $250k–350k/yr
- Location
- Menlo Park, CA
- Work type
- On-site
- Posted
- today
[
Apply on company site (opens in new tab) ](https://jobs.ashbyhq.com/periodic-labs/d3be5ecc-c4d3-4c9e-9a4d-519ab6147474/application)
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 with mid-training or pre-training at scale — big-lab experience is a strong plus.
- 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.
- The ability to calculate 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 on a big training run tracking evals and driving interventions while the run was live, not just as a peripheral contributor.
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.