{"id":741395,"url":"https://alion.io/job/hud-research-engineer-benchmarks","title":"Research Engineer, Benchmarks","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":null,"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":157000,"max_usd":339000,"period":"year","method":"role_country_seniority_unknown","sample_n":2391},"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}],"status":"live","first_seen_at":"2026-07-13T18:31:24Z","employer_posted_date":"2026-07-13","last_verified_at":"2026-09-24T09:19:16Z","board_verified":true,"closed_at":null,"days_open":72,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":72},"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 Research Engineers to build high-quality benchmarks for evaluating frontier agents on domain-specific tasks. You’ll build benchmarks that are technically rigorous, practically useful, and credible to frontier labs.\nResponsibilities\nOwn the design, implementation, and quality of HUD’s internal agent benchmarks\n\nWork with subject-matter experts to define tasks and create domain-specific benchmarks that evaluate agents on realistic workflows\n\nBuild infrastructure to reliably run models and agents against benchmark tasks\n\nDevelop metrics and analyses to understand benchmark difficulty, reliability, and failure modes\n\nValidate whether benchmark performance correlates with real-world evals, customer needs, and lab expectations\n\nWrite clear documentation and benchmark reports that make results legible and credible to technical audiences\n\nExperience\nYou may be a good fit if you have:\nProficiency in Python, Docker, and Linux environments\n\nPublished papers or written technical blogs on relevant topics such as public benchmarks and their limitations, model failure modes, etc. - please link in your application\n\nStrong understanding of what a “good benchmark” means and what makes one realistic, reliable, and useful\n\nExperience working on environments and evals\n\nCuriosity and ability to truly understand how workflows in various domains work\n\nStrong candidates may also:\nBe detail-oriented and able to spot subtle inconsistencies or edge cases in tasks\n\nBe able to reason from first principles about task design, scoring, and failure modes\n\nThrive in unstructured problem spaces\n\nEarly-stage startup experience with ability to work independently in fast-paced environments\n\nStrong communication skills for remote collaboration across time zones\n\nWe prioritize technical aptitude and learning potential over years of experience. Motivated candidates are encouraged to apply even if they don't meet all criteria.\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":3814,"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":22,"band":"cold","label":"Long shot","p_open":1,"p_active":0.303,"p_room":0.72,"age_days":72,"expected_fill_days":127,"reasons":["conf:0","velocity","win:mid","crowd:"],"computed_at":"2026-09-24T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/hud-research-engineer-benchmarks","json_url":"https://alion.io/job/hud-research-engineer-benchmarks.json","meta":{"generated_at":"2026-09-24T09:25:12Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers"}}