Research Engineer, QC Automation
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
- Build quality-control systems for training data that use human judgment rather than depending heavily on language models
- Set standards for training-data quality, then create experiments and measures to assess agent results
- Work with data vendors to trace quality problems and agent failures, giving feedback they can use to improve data creation
- Turn quality findings into dataset audits, sampling approaches, validation pipelines and feedback loops
- Add quality checks to infrastructure tools and the vendor portal to catch anomalies and edge cases
What you bring
- You can build data-validation pipelines and automated quality systems from start to finish, with little prescribed direction
- You know Python, Docker and Linux environments
- You have worked on benchmarks or evaluations and can judge task realism, rubric reliability and training value
Who this fits
This role suits an independent engineer who enjoys open-ended technical work, asking questions across domains and designing quality measures as well as implementing them. The position is full-time, with offices in San Francisco or Singapore; remote candidates need substantial overlap with either city’s time zone. Relocation and visa support is available for strong full-time candidates moving to the US or Singapore.
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 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
Experience
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 & 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!