Research Scientist/Research Engineer, Midtraining
Periodic Labs· Engineering
Menlo Park, CA$250k - $350kFull-timePosted 2w ago
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.
Responsibilities
- 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.
Qualifications
- 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.
Skills
Required:Data curationSynthetic data generationEvaluation designDistillation techniquesLarge-scale training
Last updated Oct 7, 2026. Interested? Apply on the company site.
About Periodic Labs
From bits to atoms.
Periodic Labs is an artificial intelligence research company pioneering the next leap in scientific discovery. We operate at the intersection of machine learning and scientific experimentation under the thesis that that true understanding of the universe is only possible by interacting and experimenting on it.
artificial intelligencescientific discoverymachine learningscientific experimentation
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