Environment Engineers

What You'll Do

• Design data slices and explore data shapes that expose meaningful model failure modes across domains like finance, code, and enterprise workflows
• Build and refine evaluation rubrics and reward signals for RLHF and RLVR training pipelines
• Model annotator behavior and run experiments to improve different model capabilities
• Develop quantitative frameworks for measuring dataset quality, diversity, and downstream impact on model alignment and capability
• Create and manage both real world & synthetic data pipelines
• Partner with lab research teams to translate their training objectives into concrete data and evaluation specifications

What We're Looking For

• 1-4 YOE
• Major plus if they've worked for/interned for any RL environment companies in the past or any AI safety or benchmarking orgs like METR, Artificial Analysis, etc.
• 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
• Former founders and early engineers at early stage startups are a plus. We don't filter on pedigree. We want people who can demonstrate they work hard, learn fast, and care deeply about getting the details right.

Compensation

$200k base + profit share (around 150% of base) + competitive equity