Research Engineer, Benchmarks

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 Research Engineers to build high-quality benchmarks for evaluating frontier agents on domain-specific tasks. You’ll build benchmarks that are technically rigorous, practically useful, and credible to frontier labs.

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  Own the design, implementation, and quality of HUD’s internal agent benchmarks

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  Work with subject-matter experts to define tasks and create domain-specific benchmarks that evaluate agents on realistic workflows

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  Build infrastructure to reliably run models and agents against benchmark tasks

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  Develop metrics and analyses to understand benchmark difficulty, reliability, and failure modes

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  Validate whether benchmark performance correlates with real-world evals, customer needs, and lab expectations

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  Write clear documentation and benchmark reports that make results legible and credible to technical audiences

• Proficiency in Python, Docker, and Linux environments

• Published papers or written technical blogs on relevant topics such as public benchmarks and their limitations, model failure modes, etc. - please link in your application

• Strong understanding of what a “good benchmark” means and what makes one realistic, reliable, and useful

• Experience working on environments and evals

• Curiosity and ability to truly understand how workflows in various domains work

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

• Be able to reason from first principles about task design, scoring, and failure modes

• Thrive in unstructured problem spaces

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

• Strong communication skills for remote collaboration across time zones

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.