Research Engineer, Benchmarks
About the role
HUD is building reliable, fair and open infrastructure for AI data. The role focuses on designing and implementing high-quality benchmarks to evaluate frontier agents on domain-specific tasks, including infrastructure, metrics, and reports to credibly assess performance.
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
Qualifications
- 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. (link in 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
Skills
Benefits
- Competitive compensation
- 100% covered top-of-the-line medical, dental, and vision (US and Singapore employees)
- Lunch and dinner for in-office employees
- Company-wide holiday break (Christmas Eve to New Year’s Day) on top of PTO and paid holidays
- Equinox membership, 401k, commuter benefits (US employees) and health/wellness stipend (Singapore employees)
- Unlimited access to tokens for ChatGPT, Claude Code, Cursor, etc. (no known usage limit)
- Two top-floor offices in San Francisco and Singapore
- Annual travel budget to visit either office
About hud
Platform for building RL environments and evals
HUD (YC W25) is developing agentic evals and RL environments for Computer Use Agents (CUAs) that browse the web for frontier AI labs. Our CUA Evals framework is the first comprehensive evaluation tool for CUAs. People don't actually know if AI agents are working reliably. To make AI agents work in the real world, we need detailed evals for a huge range of tasks. We're backed by Y Combinator, and work closely with frontier AI labs to provide agent evaluation and training infrastructure at scale.
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