Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will improve base models’ scientific reasoning by curating and generating data, building evaluations, and running large-scale training experiments. You will develop distillation techniques, investigate how data choices affect model intelligence, and create tools that support data, evaluation, and training work.
Requirements
- Experience training LLMs on curated mixes of trillions of tokens
- Experience supporting a large production training run on a dedicated evaluation team
- Hands-on experience with 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, evaluations, and training infrastructure
- Bachelor’s degree or similar experience
Responsibilities
- Identify, process, and curate novel scientific data sources for large-scale model training
- Generate high-quality synthetic data for scientific knowledge and reasoning
- Build evaluations that correlate with downstream scientific task performance
- Develop and apply self-distillation and on-policy distillation techniques
- Design and run large-scale training experiments
- Build tools to investigate how data choices shape model intelligence
Benefits