Lead Research Engineer, Data Quality

HUD is building infrastructure for reinforcement learning training data and evaluations for frontier AI agents. The Lead Research Engineer, Data Quality will lead the data quality team, develop systems to evaluate and improve training data, and establish scalable quality standards, validation methods, and feedback workflows.

Responsibilities:

• 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 research taste around what makes agent training data actually useful, not just superficially correct
• Mentor other research engineers to maintain a high bar for technical rigor, clarity, and execution speed