Job Description
Note: The job is a remote job and is open to candidates in USA. AfterQuery is a company that builds training data and evaluation infrastructure for leading AI labs to enhance their models. The Environment Engineer will design datasets and evaluation rubrics, working closely with research teams to improve model performance through hands-on experimentation and data analysis. 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 Skills 1-4 YOE 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 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 Benefits Profit share Equity Company Overview AfterQuery is an AI data company that builds expert-level datasets to help train and improve AI models. It was founded in 2024, and is headquartered in San Francisco, California, USA, with a workforce of 51-200 employees. Its website is