Environment Engineers at AfterQuery in San Francisco, CA

Summary: AfterQuery builds the training data and evaluation infrastructure that frontier AI labs use to make their models better. The role involves designing datasets and evaluation rubrics that influence how models learn, collaborating with research teams, and developing metrics to assess model improvements. Responsibilities: - Design data slides 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 Required Qualifications: - 1-4 YOEMajor - 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 Preferred Qualifications: - 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 - 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 Required Skills: Reinforcement Learning from Human Feedback (RLHF), Reinforcement Learning from Virtual Rewards (RLVR), Data pipeline management, Quantitative frameworks for dataset quality measurement, Model evaluation rubric design, Data slicing for failure mode analysis, Synthetic data generation, RL environment companies, AI safety/benchmarking organizations, design lightweight experiments, move fast, extract actionable insights from messy results, Work hard, Learn fast Benefits: Profit share (around 150% of base), Equity ($100-200k over 4 years)