{"id":4233472,"url":"https://alion.io/job/google-staff-software-ml-engineer-search-discover-feed-recommendation","title":"Staff Software ML Engineer, Search Discover Feed Recommendation","company":{"id":82,"name":"Google","domain":"google.com","url":"https://alion.io/company/google","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":{"grade":"B","score":78,"open_postings":131,"ghost_share":0.038,"stale_share":0.763,"repost_share":0.038,"time_to_fill_p50_days":30,"computed_at":"2026-11-12T10:38:37Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Mountain View, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Context Engineering","optional":false},{"name":"DPO","optional":false},{"name":"Gemini","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Recommender Systems","optional":false},{"name":"RLHF","optional":false}],"status":"live","first_seen_at":"2026-11-13T06:39:44Z","employer_posted_date":"2026-11-13","last_verified_at":"2026-11-13T17:39:50Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"About the job\nGoogle's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.The Discover Feed Recommendation team leads by recommending AI-generated content (AIGC) to fulfill user interests. As a Staff Machine Learning Engineer focusing on Search AIGC Quality, you will be responsible for AIGC quality control, topic identification, context engineering, and content understanding. You will build the agentic loops and evaluation frameworks needed to ensure factuality, freshness, coherence, and tone for AI-generated content that empowers users across Google's proactive surfaces like Discover and Notifications.\nJoin Google Search AI and work at the intersection of industry leadingglobal scale. We apply breakthrough research, leveraging models like Gemini, to launch high-impact products that help billions ask any question and get things done. If you want to drive the future of AI in Search and work with world-class experts to deliver tangible, global impact and continuous innovation, this is the place.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.\nUS: $207000 - $300000 (USD) + 20% bonus target + equity + benefits\nLearn more about benefits at Google.\nResponsibilities\nBuild advanced AI-generated content (AIGC) quality frameworks and agentic flows to generate content for proactive surfaces like Discover and Notifications.\nDevelop AI-generated content through advanced context engineering and agentic feedback loops to identify and deliver engaging stories globally.\nConduct advanced quality evaluations and leverage downstream dense recommendation signals and feedback to improve the performance of the AIGC stack.\nResolve key system-level bottlenecks in recommending and serving AIGC content to optimize efficiency for low-latency surfaces.\nNavigate high 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