About the role
About HUD
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 an Applied Research Engineer to own implementation. Our data buyers and sellers often have time-sensitive asks such as troubleshooting and running evals or cleaning data at scale. You’ll take the lead on diagnosing ambiguous technical problems and resolving them. This work is broad and hands-on - the right fit is a strong generalist AI engineer who can move quickly and wants to work closely with frontier AI labs and data vendors.
Responsibilities
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Own technical deployment requests from frontier AI labs, data vendors, and internal teams from triage to completion
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Ask the right questions to clarify ambiguous asks and identify what actually needs to be done
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Build tools and one-off pipelines to solve urgent customer or partner problems
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Coordinate with research and GTM teams to unblock deployments
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Balance speed and quality in situations where customers need fast turnaround and the path is not fully specified
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Document recurring issues and turn repeated manual work into reusable tools or processes
Experience
You may be a good fit if you have:
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Proficiency in Python, Docker, and Linux environments
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Experience working on benchmarks and evals - you can reason about what makes a task realistic, a rubric reliable, an environment usable, and a trajectory useful for RL training
