Lead Research Engineer, Data Quality

About the role

ABOUT HUD

HUD https://www.hud.ai/'s mission is to build reliable, fair and open infrastructure for AI data. We want data to be valuable for the people who create it and trustworthy for the labs that train on it. Our team is a quickly growing group of researchers, engineers and operators building the economy that shapes what AI will become. Backed by $16M from top VCs and YC (W25), our marketplace and platform are used by startups, Fortune 500 companies and frontier labs.

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.

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

EXPERIENCE

You may be a good fit if you have:

- 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

Strong candidates may also have:

- 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

- Be comfortable designing metrics, experiments, and QA/QC processes, not just executing them

- Early-stage startup experience with ability to work independently in fast-paced environments

- Be detail-oriented and able to spot subtle inconsistencies or edge cases in data