Research Engineer, QC Automation
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Role Overview
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.
Key Skills for This Role
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About HUD
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.
About the role
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.
Responsibilities
- 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
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
Be detail-oriented and able to spot subtle inconsistencies or edge cases in data
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
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 : ~15 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 high revenue growth. We’re scaling profitably and quickly to meet very strong demand.
Logistics
Employment : Full-time.
Location : On-site in the San Francisco Bay Area.
Visa Sponsorship : We provide support for relocation and visas for strong full-time candidates to the US.
Timeline : Applications are rolling. The process is 2 technical interviews and a 2-3 day work trial.
What we offer
- Competitive compensation based on experience and location
- 100% covered top-of-the-line medical, dental, and vision from Blue Shield of CA
- Lunch and dinner when you’re in the office
- 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
- 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 recruiting@hud.so or elsewhere if we missed your application!
About HUD
Private AI platform building reinforcement-learning environments, evaluating models, and delivering post-training data to frontier AI labs.
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