Periodic Labs

Research Engineer - Midtraining

About

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.

Who can you reach out to