Job description

ABOUT HUD HUD hud.ai'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 a Lead Research Engineer, Data Quality to own how HUD measures, improves, and scales the quality of training data for frontier agents. You’ll lead the data quality team in building the systems that evaluate thousands of tasks across RL environments, synthetic data, benchmarks, and domain-specific workflows. RESPONSIBILITIES - Lead HUD’s data quality strategy including building QC systems, defining and enforcing quality standards, and designing experiments to grade agent outputs - Develop new methods for validating synthetic data at scale, such as failure-mode analysis, task mutation checks, and trajectory auditing - Partner with research engineers, domain experts, and data vendors to diagnose quality issues and improve data generation workflows - Turn qualitative research insights into production systems, internal tools, dashboards, validation pipelines, and feedback loops - Help build internal research taste around what makes agent training data actually useful, not just superficially correct - Mentor other research engineers to maintain a high bar for technical rigor, clarity, and execution speed EXPERIENCE You may be a good fit if you have: - Advanced proficiency in Python, Docker, and Linux environments - Deep intuition for data quality - you can reason about what makes tasks realistic, learnable, diverse, reliable, and useful for training - Experience building QC systems, evals, benchmarks, synthetic data pipelines, validation workflows, or model evaluation infrastructure - Comfort working across messy human and technical systems, including domain experts, vendors, generated data, model outputs, graders, and infrastructure - Strong written communication and the ability to explain methodology clearly to researchers, engineers, labs, and external audiences Strong candidates may also have: - Experience leading teams on ambiguous technical projects from problem definition through implementation and iteration - Experience working with subject-matter experts to capture domain judgment and convert it into scalable review or generation systems - Be comfortable designing metrics, experiments, and QA/QC processes, not just executing them - Early-stage startup experience with ability to work independently in fast-paced environments - Be detail-oriented and able to spot subtle inconsistencies or edge cases in data 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 - 401k with a 4% match - Other perks including an Equinox membership 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): $200,000 - $295,000 - Singapore: $150,000 - $225,000 - Rest of world: $120,000 - $225,000 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!

Requirements

Must-haves

Lead HUD’s data quality strategy including building QC systems, defining and enforcing quality standards, and designing experiments to grade agent outputs
Develop new methods for validating synthetic data at scale, such as failure-mode analysis, task mutation checks, and trajectory auditing
Partner with research engineers, domain experts, and data vendors to diagnose quality issues and improve data generation workflows
Turn qualitative research insights into production systems, internal tools, dashboards, validation pipelines, and feedback loops
Help build internal research taste around what makes agent training data actually useful, not just superficially correct
Mentor other research engineers to maintain a high bar for technical rigor, clarity, and execution speed
You may be a good fit if you have:
Advanced proficiency in Python, Docker, and Linux environments
Deep intuition for data quality - you can reason about what makes tasks realistic, learnable, diverse, reliable, and useful for training
Experience building QC systems, evals, benchmarks, synthetic data pipelines, validation workflows, or model evaluation infrastructure
Comfort working across messy human and technical systems, including domain experts, vendors, generated data, model outputs, graders, and infrastructure
Strong written communication and the ability to explain methodology clearly to researchers, engineers, labs, and external audiences
Experience leading teams on ambiguous technical projects from problem definition through implementation and iteration
Experience working with subject-matter experts to capture domain judgment and convert it into scalable review or generation systems
Be comfortable designing metrics, experiments, and QA/QC processes, not just executing them
Early-stage startup experience with ability to work independently in fast-paced environments
Be detail-oriented and able to spot subtle inconsistencies or edge cases in data
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.
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.

Nice-to-haves

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Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401k

Pto

Wellness