AfterQuery is an applied research lab that curates high-quality datasets and reinforcement learning environments to encode and scale professional expertise for AI labs and enterprises.
ABOUT AFTERQUERY AfterQuery https://www.afterquery.com/ is an applied research lab curating data solutions for foundation model development. We serve every frontier AI lab with the mission of delivering the best data to power the best models. In doing so, we can make expertise that once took a lifetime to build available to anyone who needs it. Our customers are the ones building the foundation models themselves and our work sits directly in the loop of how those systems improve. This is a rare opportunity to join a company at a defining moment in AI. Since raising our $30M Series A at a $300M valuation, AfterQuery has grown well over a $100M revenue run rate. We're based in San Francisco and backed by leading investors including Altos Ventures, BoxGroup, and Y Combinator and angels from Google DeepMind, OpenAI, Anthropic, Meta Superintelligence Labs, and Microsoft AI. The Role As a SWE (Environments), you will design the datasets and evaluation rubrics that directly influence how frontier models learn. You'll work hands-on with research teams at top AI labs, experimenting with data collection strategies, diagnosing model failure modes, and developing the metrics that determine whether a model is actually improving. You'll go from hypothesis to live experiment quickly, and your output will feed directly into model training runs at scale. Day to day, you will design data slices that expose meaningful failure modes across domains like finance, code, and enterprise workflows. You will build and refine reward signals for RLHF and RLVR pipelines. You will develop quantitative frameworks for measuring dataset quality, diversity, and downstream impact on alignment and capability. You will partner with lab research teams to translate their training objectives into concrete data and evaluation specifications. What You'll Do - 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 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 Structure: $200k base + profit share (around 150% of base) + competitive equity