{"id":2290222,"url":"https://alion.io/job/luvu-inc-ai-engineer","title":"AI Engineer","company":{"id":4398972,"name":"Basis","domain":"withbasis.co","url":"https://alion.io/company/withbasis-2","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":95,"open_postings":28,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":73,"computed_at":"2026-10-31T05:58:55Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Seattle, United States","San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":174000,"max_usd":387000,"period":"year","method":"role_country_seniority_unknown","sample_n":4425},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"LLM Evaluation","optional":false}],"status":"live","first_seen_at":"2026-07-29T06:22:10Z","employer_posted_date":"2026-07-29","last_verified_at":"2026-11-01T03:08:43Z","board_verified":true,"closed_at":null,"days_open":94,"trust":{"level":"ok","repost_count":1,"flags":["company_stale"],"days_open":93},"description":"About the Company\nKanu AI is your organization’s AI coworker that deploys in your own cloud, continuously learns from your data, team intelligence, and outputs, and uses that knowledge to encode and maintain workflows that mirror how your people work.\nThe Role\nWe're hiring an AI engineer to own the AI systems at the heart of the product. You'll work directly with the founders and own the full loop: data, training, evaluations, and shipping into production.\nWhat You'll Do\nBuild and own the ML stack-data pipelines, infrastructure, agent optimization, evaluations, tuning\n\nShip AI features into production constantly and iterate against real usage\n\nImprove the evaluation harnesses that tell us whether the product is actually getting better\n\nTrack the research frontier and apply it pragmatically-novelty only where it wins\n\nWhat We're Looking For\nEvidence you've shipped ML that real users touched-in production, research, or serious projects\n\nStrong fundamentals across the stack, not just notebooks\n\nPragmatism: you pick the boring approach that works over the clever one that might\n\nComfort owning systems end to end with no ML team behind you\n\nCompensation & Equity\nEarly-stage compensation: a meaningful equity stake plus salary. 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