Backed by Y Combinator.
About the role
We’re looking for a Lead Research Engineer, Data Quality to own how HUD measures, improves, and scales the quality of training data for frontier agents. You’ll lead the data quality team in building the systems that evaluate thousands of tasks across RL environments, synthetic data, benchmarks, and domain-specific workflows. Lead HUD’s data quality strategy including building QC systems, defining and enforcing quality standards, and designing experiments to grade agent outputs
What they're looking for
- Advanced proficiency in Python, Docker, and Linux environments
- Deep intuition for data quality - you can reason about what makes tasks realistic, learnable, diverse, reliable, and useful for training
- Experience building QC systems, evals, benchmarks, synthetic data pipelines, validation workflows, or model evaluation infrastructure
- Comfort working across messy human and technical systems, including domain experts, vendors, generated data, model outputs, graders, and infrastructure
- Strong written communication and the ability to explain methodology clearly to researchers, engineers, labs, and external audiences
More about this role
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 $16M from top VCs and were YC W25.
We’re looking for a Lead Research Engineer, Data Quality to own how HUD measures, improves, and scales the quality of training data for frontier agents. You’ll lead the data quality team in building the systems that evaluate thousands of tasks across RL environments, synthetic data, benchmarks, and domain-specific workflows.
Lead HUD’s data quality strategy including building QC systems, defining and enforcing quality standards, and designing experiments to grade agent outputs
Develop new methods for validating synthetic data at scale, such as failure-mode analysis, task mutation checks, and trajectory auditing
Partner with research engineers, domain experts, and data vendors to diagnose quality issues and improve data generation workflows
Turn qualitative research insights into production systems, internal tools, dashboards, validation pipelines, and feedback loops
Help build internal...
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