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 automate QC for training data created by companies using HUD’s infrastructure. You’ll build the systems that scale quality to help us meet our continued strong demand.
Create QC systems based on true understanding and human judgement, without relying heavily on LLMs
Define and enforce quality standards for training data
Design experiments and metrics to grade agent outputs
Partner with data vendors to debug quality issues and diagnose agent failure modes, provide actionable feedback, and improve their data generation processes
Translate QC learnings into systems for auditing supplier-generated datasets, including sampling strategies, validation pipelines (rule-based and model-assisted), and feedback loops
Continuously integrate QC learnings into infrastructure tools and data vendor portal to reduce anomalies, inconsistencies, and edge cases
You may be a good fit if you have:
Proficiency in Python, Docker, and Linux environments
Strong understanding of what “good data” means and how to measure it
Genuine curiosity of different domains and great at asking questions to understand them
Built scalable data validation pipelines and automated QA/QC systems end-to-end without a fully prescribed roadmap
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
Early-stage startup experience with ability to work independently in fast-paced environments
Strong candidates may also:
Have knowledge of statistics
Have strong written and verbal communication skills for collaboration with our partners and team members across time zones
Be comfortable designing metrics, experiments, and QA/QC processes, not just executing them
Have experience with existing benchmarks and can reason about how to construct tasks in new evals
Thrive in unstructured problem spaces
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 Size: ~25 people currently, mostly full-time in-person, but some remote.
Our team: Our team includes 4 International Olympiad medalists (IOI, ILO, IPhO), 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.
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.
Competitive compensation
100% covered top-of-the-line medical, dental, and vision from Blue Shield of CA (US employees)
Lunch and dinner when you’re in the office (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)
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.
Due to high volume, we may not actively respond to every application, but feel free to contact us at [email protected] or elsewhere if we missed your application!
Job details are sourced from the employer's original posting.
Open job postingAbout the company
HUD (YC W25) is developing agentic evals for Computer Use Agents (CUAs) that browse the web. Their CUA Evals framework is the first comprehensive evaluation tool for CUAs. Their mission is to make AI agents work in the real world by providing detailed evaluations for a huge range of tasks. They are backed by Y Combinator and work closely with frontier AI labs to provide agent evaluation infrastructure.