{"id":741390,"url":"https://alion.io/job/hud-lead-research-engineer-data-quality","title":"Lead Research Engineer, Data Quality","company":{"id":678875,"name":"HUD","domain":"hud.so","url":"https://alion.io/company/hud-3","size_band":"11-50","is_staffing_agency":false,"is_intermediary":false,"ats_vendor":"Ashby","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States","Singapore"],"countries":["US","SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":174000,"max_usd":369000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":374},"experience_years_min":null,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"ChatGPT","optional":false},{"name":"Claude Code","optional":false},{"name":"Cursor","optional":false},{"name":"Docker","optional":false},{"name":"Linux","optional":false},{"name":"Python","optional":false},{"name":"Synthetic Data","optional":false}],"status":"live","first_seen_at":"2026-07-28T22:46:10Z","employer_posted_date":"2026-07-28","last_verified_at":"2026-09-24T09:19:16Z","board_verified":true,"closed_at":null,"days_open":57,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":57},"description":"About HUD\nHUD 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.\nAbout the role\nWe’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.\nResponsibilities\nLead HUD’s data quality strategy including building QC systems, defining and enforcing quality standards, and designing experiments to grade agent outputs\n\nDevelop new methods for validating synthetic data at scale, such as failure-mode analysis, task mutation checks, and trajectory auditing\n\nPartner with research engineers, domain experts, and data vendors to diagnose quality issues and improve data generation workflows\n\nTurn qualitative research insights into production systems, internal tools, dashboards, validation pipelines, and feedback loops\n\nHelp build internal research taste around what makes agent training data actually useful, not just superficially correct\n\nMentor other research engineers to maintain a high bar for technical rigor, clarity, and execution speed\n\nExperience\nYou may be a good fit if you have:\nAdvanced proficiency in Python, Docker, and Linux environments\n\nDeep intuition for data quality - you can reason about what makes tasks realistic, learnable, diverse, reliable, and useful for training\n\nExperience building QC systems, evals, benchmarks, synthetic data pipelines, validation workflows, or model evaluation infrastructure\n\nComfort working across messy human and technical systems, including domain experts, vendors, generated data, model outputs, graders, and infrastructure\n\nStrong written communication and the ability to explain methodology clearly to researchers, engineers, labs, and external audiences\n\nStrong candidates may also have:\nExperience leading teams on ambiguous technical projects from problem definition through implementation and iteration\n\nExperience working with subject-matter experts to capture domain judgment and convert it into scalable review or generation systems\n\nBe comfortable designing metrics, experiments, and QA/QC processes, not just executing them\n\nEarly-stage startup experience with ability to work independently in fast-paced environments\n\nBe detail-oriented and able to spot subtle inconsistencies or edge cases in data\n\nTeam & company details\nTeam Size: ~25 people currently, mostly full-time in-person, but some remote.\n\nOur team: Our team includes 4 International Olympiad medalists (IOI, ILO, IPhO), serial AI startup founders, and researchers with publications at ICLR, NeurIPS, etc.\n\nCompany stage: We have 8 figures in funding and are scaling profitably and quickly to meet very strong demand.\n\nLogistics\nEmployment: Full-time.\n\nLocation: 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.\n\nVisa Sponsorship: We provide support for relocation and visas for strong full-time candidates to the US or Singapore.\n\nTimeline: Applications are rolling. The process is 2 technical interviews and a 2-3 day work trial.\n\nWhat we offer\nCompetitive compensation\n\n100% covered top-of-the-line medical, dental, and vision from Blue Shield of CA (US employees)\n\nLunch and dinner when you’re in the office (in-office employees)\n\nCompany-wide holiday break (Christmas Eve to New Year’s Day) on top of PTO and paid holidays\n\nOther perks including an Equinox membership, 401k, and commuter benefits (US employees)\n\nUnlimited* 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.\n\nDue to high volume, we may not actively respond to every application, but feel free to contact us at  or elsewhere if we missed your application!","description_format":"text","description_chars":4229,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["401k plan"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":true,"industries":[],"lifecycle":[{"event":"open","at":"2026-09-11T12:44:23Z"}],"liveness":{"score":9,"band":"cold","label":"Long shot","p_open":1,"p_active":0.249,"p_room":0.35,"age_days":57,"expected_fill_days":7,"reasons":["conf:0","velocity","win:tail"],"computed_at":"2026-09-24T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/hud-lead-research-engineer-data-quality","json_url":"https://alion.io/job/hud-lead-research-engineer-data-quality.json","meta":{"generated_at":"2026-09-24T09:26:03Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers"}}