Research Manager

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.

About the role

We’re looking for a Research Manager to lead research that makes HUD’s agent training data and evals more useful for improving frontier models. You’ll lead research engineers through ambiguous projects, and turn findings into methods that can be used at scale. You’ll stay close to the technical work while helping the team choose the right questions, run rigorous experiments, and deliver results. This role calls for an understanding of how and why models train: which signals teach useful behavior, where apparent progress is misleading, and how data and eval design can change outcomes.

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

Qualifications

• Experience leading technical research projects to completion, from an open question to evidence, a decision, and a working result
• Experience directly managing and mentoring researchers or research engineers while remaining engaged in technical work
• A strong understanding of machine learning and reinforcement learning, including how training objectives, data, and feedback shape model behavior
• Experience with agent training data, evals, benchmarks, synthetic data, or model evaluation infrastructure
• Sound experimental judgment: you can distinguish a useful training signal from a task or metric that only looks convincing
• Strong written communication and the ability to explain methods and findings to researchers, engineers,