About HUD

HUD'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 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.

Responsibilities

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

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

• Build infrastructure to reliably run models and agents against benchmark tasks

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

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

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

Experience

You may be a good fit if you have:

• 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

Strong candidates may also:

• 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