About the role
About AfterQuery
AfterQuery builds the training data and evaluation infrastructure that frontier AI labs use to make their models better. We work with the world's leading labs to design high signal datasets and run rigorous evaluations that go beyond static benchmarks. We are a small, early team (post Series A) where individual contributors have a direct impact on how the next generation of models learn and improve.
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
