Software Engineer, Data Quality

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.

We’re looking for a Software Engineer, Data Quality to build the tools that help HUD’s Quality and Intelligence (QNI) team assess and improve training data and evals for frontier agents. You’ll turn hands-on quality workflows into reliable internal products, then bring the most useful capabilities into HUD’s production platform.

You’ll work closely with QNI and research engineers across task review, trajectory inspection, grader checks, quality metrics, and feedback to data creators. The work spans interfaces, backend services, and data workflows, with a focus on making quality issues easier to find, understand, and fix.

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  Build full-stack tools for reviewing tasks, inspecting agent trajectories, checking graders, and investigating data quality issues

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  Create dashboards and metrics that show quality trends, failure modes, and the health of review and validation workflows

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  Design backend services, APIs, and data workflows that connect quality checks with task creation, evaluation, and feedback to data creators

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  Work with QNI and research engineers to turn evolving review methods into clear, efficient workflows that scale beyond manual analysis

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  Move proven internal tools into production, improving their reliability, usability, and observability as adoption grows

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  Investigate issues in live workflows and use what you learn to improve the platform and prevent repeat failures

• Strong software engineering fundamentals and the ability to build across frontend, backend, and data systems

• Proficiency in Python and a modern web stack such as TypeScript and React, or comparable tools

• Built internal or user-facing products end-to-end, from understanding a workflow through shipping and improving it

• Sound judgment about APIs, data models, and production systems, including how to make them reliable and easy to debug

• An ability to turn ambiguous quality problems into useful interfaces, automation, and measurable checks

• Clear communication and comfort working closely with research and quality teams to understand how people use the tools you build

• Built tooling for data review, annotation, evals, benchmarks, or ML workflows

• Worked with agent traces, graders, reward signals, or RL training data

• Designed dashboards, observability tools, or review workflows for complex datasets and pipelines

• Improved a prototype or internal tool until it was ready for wider production use

We prioritize technical aptitude and learning potential over years of experience. Motivated candidates are encouraged to apply even if they don't meet all criteria.