Research Engineer, Benchmarks
At a glance
Mid-level AI Research role at HUD. San Francisco · full-time · $100,000–$230,000 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Design and build internal benchmarks that test agents on realistic, domain-specific workflows
- Work with subject-matter experts to turn domain workflows into tasks agents can be evaluated against
- Build reliable infrastructure for running models and agents on benchmark tasks
- Analyze benchmark difficulty, consistency and failure patterns, then check results against real-world evaluations and customer needs
- Write technical reports and documentation that make benchmark results clear and credible
What you bring
- You can work proficiently with Python, Docker and Linux
- You have published research or written technical material on topics such as benchmarks or model failure modes
- You understand how to design evaluations that are realistic, reliable and useful, and have worked with environments and evaluations
- You can dig into how workflows operate across different domains
Who this fits
This mid-level role suits a technically strong researcher or engineer who can work through open-ended task design and communicate findings clearly. The position is full-time and based in San Francisco or Singapore, with remote work possible if you can overlap substantially with either office’s time zone. The process includes two technical interviews and a work trial lasting two to three days.
From the employer
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
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.
Team & company details
Team Size: ~25 people currently, mostly full-time in-person, but some remote.
Our team: Our team includes 4 International Olympiad medalists (IOI, ILO, IPhO), the former #1 ranked Catan player in the world, serial AI startup founders, and researchers with publications at ICLR, NeurIPS, etc.
Company stage: We have 8 figures in funding and are scaling profitably and quickly to meet very strong demand.
Logistics
Employment: Full-time.
Location: We have offices in San Francisco or Singapore but are open to remote candidates who can work hours that 70-80% overlap with either San Francisco or Singapore time zones.
Visa Sponsorship: We provide support for relocation and visas for strong full-time candidates to the US or Singapore.
Timeline: Applications are rolling. The process is 2 technical interviews and a 2-3 day work trial.
What we offer
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
Other perks including an Equinox membership, 401k, and commuter benefits (US employees) and a health/wellness stipend (Singapore employees)
Unlimited* access to tokens for ChatGPT, Claude Code, Cursor, etc. *By unlimited, we mean no one on our token usage leaderboard has ever hit a limit. So we have no idea what the limit is.
Two top-floor offices in San Francisco’s Union Square and Singapore’s Raffles Place
Annual travel budget to visit either office
Compensation
Actual offers are adjusted for experience and location, but our base salary bands are
San Francisco (and other major US cities): $135,000 - $230,000
Singapore: $100,000 - $175,000
Rest of world: $100,000 - $175,000
Due to high volume, we may not actively respond to every application, but feel free to contact us at recruiting@hud.so or elsewhere if we missed your application!