Frontier AI Research Engineer for Scientific Discovery

Periodic Labs; Menlo Park (CA); On-site; USD 250,000 - 350,000; Full time

Job summary

Periodic Labs in Menlo Park, CA seeks a Midtraining Research Engineer to advance scientific reasoning in frontier models. You will curate data, build evals, and run large-scale training experiments, laying groundwork for future pre-training efforts.

You will collaborate with RL researchers, physicists, and chemists to design scalable workflows across thousands of GPUs and develop tools to analyze 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.

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.

Skills

LLMs training

Evals team experience

Distillation methods

Scaling laws

Training infrastructure

Education

Bachelor's degree or equivalent

Job description

Periodic Labs in Menlo Park, CA seeks a Midtraining Research Engineer to advance scientific reasoning in frontier models. You will curate data, build evals, and run large-scale training experiments, laying groundwork for future pre-training efforts.

You will collaborate with RL researchers, physicists, and chemists to design scalable workflows across thousands of GPUs and develop tools to analyze how data choices shape model intelligence.