Research Manager

HUD is building infrastructure for reinforcement learning training data and evaluations for frontier AI agents. The Research Manager will lead research on improving agent training data and evaluations, manage and mentor research engineers, design rigorous experiments, and translate findings into scalable methods, tools, and quality standards.

Responsibilities:

• Set the research direction for data quality, including how HUD measures whether tasks, trajectories, rewards, and evals are reliable and useful for training agents
• Lead research engineers from problem definition through experiments, implementation, and clear conclusions; coach them to strengthen technical judgment and execution
• Design and review experiments that connect model behavior and failure modes to data, environment, and reward design
• Develop methods for validating and improving training data at scale, including trajectory audits, grader checks, and feedback loops
• Partner with research engineers, domain experts, and data vendors to turn research insights into better workflows, tools, and quality standards
• Communicate findings and tradeoffs clearly so the team can prioritize work and apply what it learns across research areas