Lead Research Engineer, Data Quality

About the role

As Lead Research Engineer, Data Quality you will own HUD's data-quality strategy, leading a team that builds systems to measure, improve and scale the quality of training data for frontier agents across RL environments, synthetic data, benchmarks and domain-specific workflows.

About the company

HUD builds infrastructure for creating reinforcement-learning training data and evaluation suites for frontier AI agents, and runs a marketplace selling these assets to frontier labs. Its platform serves frontier labs, Fortune 500 companies and startups. The YC-W25 startup has raised $16M, has eight-figure revenue growth, and a team of ~15 engineers and researchers.

Requirements

• Advanced proficiency in Python, Docker, and Linux environments
• Deep intuition for data quality, able to reason about realism, learnability, diversity, reliability and usefulness of tasks
• 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 skills and ability to explain methodology clearly to researchers, engineers, labs and external audiences
• Experience leading teams on ambiguous technical projects from problem definition through implementation and iteration
• Experience working with subject-matter experts to capture domain judgment and convert it into scalable review or generation systems
• Ability to design metrics, experiments and QA/QC processes, not just execute them
• Early-stage startup experience with ability to work independently in fast-paced environments
• Detail-oriented with ability to spot subtle inconsistencies or edge cases in data