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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.

Listed by Periodic Labs for a position based in the United States. Employers on this board attest they are hiring domestically.

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