The RoleYour job is to
prove that our data works. You will design and run training experiments that isolate the impact of our datasets on model behavior. This includes SFT and RL-based post-training, where you'll measure how different data sources shift capability, generalization, and alignment. Working closely with partner labs, you will turn our datasets into clear, defensible evidence:
this data this improvement under these conditions. This is experimental, high-leverage work.
What You'll Do- Run controlled SFT and RL experiments to measure the impact of our datasets on model performance.
- Help build public evals and new data types that push the frontier.
- Publish external-facing research, blog posts, and technical reports.
- Work with internal SPLs to iterate on data quality based on your results.
What We're Looking For- Strong familiarity with LLM training and evaluation methodologies.
- Genuine obsession with how data structure, selection, and quality drive model behavior.
- Ability to design lightweight experiments, move fast, and extract actionable insights from messy results.
- Comfort working across domains (you'll touch finance, software engineering, policy, and more).
- A bias toward building over theorizing.
- Great candidates are undergrad research or master's research (but haven't done a phd).
Compensation Structure:- Annual target cash compensation of $250-450K + meaningful equity
- Comprehensive benefits (UberEats and ride share stipend, comped Equinox, 401K with match, health, dental, and vision insurance)
