{"id":2015030,"url":"https://alion.io/job/airbnb-senior-staff-machine-learning-engineer-data-eval-2","title":"Senior Staff Machine Learning Engineer (Data & Eval)","company":{"id":1762392,"name":"Airbnb","domain":"airbnb.co.uk","url":"https://alion.io/company/airbnb-co-uk","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Welcome to the Jungle","truth_index":{"grade":"B","score":84,"open_postings":7,"ghost_share":0,"stale_share":0.857,"repost_share":0,"time_to_fill_p50_days":9,"computed_at":"2026-10-13T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":{"min":244000,"max":335500,"currency":"USD","period":"year","gross":null,"usd_annual":335500},"salary_estimate":null,"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"Agile","optional":false},{"name":"Function Calling","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"Synthetic Data","optional":false},{"name":"Tool Use","optional":false}],"status":"live","first_seen_at":"2026-08-29T00:02:59Z","employer_posted_date":"2026-10-07","last_verified_at":"2026-10-13T16:34:35Z","board_verified":true,"closed_at":null,"days_open":45,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":45},"description":"Join Airbnb as a Senior Staff Machine Learning Engineer, where you will set the technical direction and lead execution for ML evaluation and the data flywheel powering CSxAI products. You will work with large-scale structured and unstructured data, collaborate with cross-functional partners, and drive technical decisions and architecture for evaluation and data infrastructure. This role requires 10+ years of industry experience, a PhD in a related field, and a strong background in ML fundamentals and best practices.\nMissions\nSet technical direction and lead execution for ML evaluation and the end-to-end data flywheel powering CSxAI products.\nDefine how to measure quality, turn feedback into learning signals, and continuously improve models and products.\nPartner closely with product, engineering, design, and operations to build evaluation systems that are trusted, scalable, and actionable.\nProfil recherché\n - Technical Proficiency:- Industry Experience: 10+ years building, testing, and shipping ML/AI systems end-to-end; including 2+ years of experience with GenAI/LLM systems in production\n- Deep expertise in evaluation methodology (offline/online alignment, metric design, human-in-the-loop evaluation, A/B testing, power analysis, regression testing)\n- Educational Background: PhD in Computer Science, Mathematics, Statistics, or related technical field (or equivalent practical experience)\n- Leadership Experience: 5+ years leading large, ambiguous technical initiatives as a senior IC, influencing roadmap and engineering/science direction across teams\n- Solid ML fundamentals and best practices (model selection, training/serving, monitoring, reliability, and model lifecycle management)\n- Hands-on experience with GenAI systems, including orchestration, retrieval, tool calling, memory, etc\n- Experience building data pipelines and quality systems (labeling workflows, dataset curation, versioning, monitoring, and governance)\n- Continuous Learner: Strong curiosity and ability to absorb new techniques (e.g., judge models, preference optimization, synthetic data generation) and apply them pragmatically\n- Infrastructure & Quality at Scale: Experience building robust evaluation platforms for agent behavior validation, safety/guardrails, and continuous improvement\n- Agile Practice for Applied AI: Proven ability to take evaluation and data 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