About the role
About HUD
HUD is building infrastructure to create RL training data and evals for frontier AI agents, as well as a marketplace to sell these to frontier labs through the HUD marketplace. Our platform is used by frontier labs, Fortune 500 companies, and startups. We’ve raised $15M from top VCs and were YC W25.
About the role
We're looking for research engineers to help build out QA for training data created by companies using HUD’s infrastructure. You’ll build the systems that scale quality to help us meet our continued strong demand.
Responsibilities
- Define and enforce quality standards for training data
- Build tooling and workflows to audit supplier-generated datasets, including sampling strategies, validation pipelines (rule-based and model-assisted), and feedback loops
- Determine if and how human-in-the-loop review workflows can be used to optimize QA
- Partner with data vendors to debug quality issues, provide actionable feedback, and improve their data generation processes
- Continuously integrate QA learnings into infrastructure tools and data vendor portal to reduce anomalies, inconsistencies, and edge cases
Experience
You may be a good fit if you have:
- Proficiency in Python, Docker, and Linux environments
- Worked with large-scale datasets
- Evidence of rapid learning and adaptability in technical environments (e.g., programming competitions)
- Startup experience in early-stage technology companies with ability to work independently in fast-paced environments
