Environment Engineers
Designs datasets, evaluation rubrics, and reward signals for RLHF/RLVR pipelines to expose model failure modes and improve frontier AI capabilities. Partners with AI labs; requires 1-4 YOE and passion for data-driven model behavior.
About the job
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
Skills
RLHF, Rlvr, Data Pipelines, Synthetic Data, Evaluation Frameworks, Dataset Quality Metrics, Ai Benchmarking, Model Alignment, Data Slices, Quantitative Analysis
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