About the role
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
Your job is to prove that our data works. You will design and run training experiments that isolate the impact of our datasets on model behavior. This includes SFT and RL-based post-training, where you’ll measure how different data sources shift capability, generalization, and alignment. Working closely with partner labs, you will turn our datasets into clear, defensible evidence: this data → this improvement → under these conditions. This is experimental, high-leverage work.
What You'll Do
- Run controlled SFT and RL experiments to measure the impact of our datasets on model performance.
- Quantify lift across capabilities (reasoning, tool use, long-horizon tasks, domain-specific workflows).
- Communicate your findings with partner labs to help drive sales.
- Work with internal SPLs to iterate on data quality based on your results.
What We're Looking For
- Strong familiarity with LLM training and evaluation methodologies.
- 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.
- Comfort working across domains (you'll touch finance, software engineering, policy, and more).
- A bias toward building over theorizing.
- Great candidates are undergrad research or master's research (but haven't done a phd).
