{"id":3742754,"url":"https://alion.io/job/tavily-senior-ml-engineer-search-optimization-agentic-search","title":"Senior ML Engineer, Search Optimization (Agentic Search)","company":{"id":6397,"name":"Tavily","domain":"tavily.com","url":"https://alion.io/company/tavily","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"A","score":87,"open_postings":3,"ghost_share":0,"stale_share":0.333,"repost_share":0,"time_to_fill_p50_days":62,"computed_at":"2026-11-10T06:52:35Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Tel Aviv, Israel"],"countries":["IL"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Embeddings","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Neo4j","optional":false},{"name":"NER","optional":false},{"name":"Python","optional":false},{"name":"Reranking","optional":false}],"status":"live","first_seen_at":"2026-11-04T10:25:34Z","employer_posted_date":"2026-11-04","last_verified_at":"2026-11-10T09:25:59Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"description":"The Product\nIn a rapidly evolving world, trust in AI depends on AI agents being grounded in fresh, verified real-world data. Search is the foundation that makes this possible.\nWe are building an agent-native search platform designed specifically for AI systems rather than human users. Our product provides programmatic, low-latency, and observable search APIs that AI agents use to retrieve, filter, and reason over real-world information at scale.\nThe Role\nAs a Senior ML Engineer focused on Search Optimization, you will work on improving the quality of our search product across the all parts of the search stack.\nYou will work on problems such as query understanding, query reformulation and expansion, retrieval, ranking, reranking, and result selection. Your goal will be to identify where search quality is lost, develop better approaches, and turn them into measurable improvements in production.\nThis is an applied ML and information-retrieval role combining experimentation with production impact. You will work with real-world queries, large-scale search systems, and evaluation signals to improve relevance, recall, freshness, and overall result quality.\nIn this position, your responsibility will be to\nDesign, implement, and operate the retrieval system for a search vertical\n\nConnect and tune the data pipeline, from ingestion to relevance tuning\n\nBuild knowledge-graph and entity-resolution layers: entity linking / NER, ontologies, and graph databases (Neo4j or similar)\n\nDevelop structured-extraction pipelines over messy, unstructured domain data\n\nReason about freshness and trust: model how confident we are in a fact and how stale it has become before we serve it\n\nDefine evaluation and quality metrics for relevance and drive measurable improvements\n\nCollaborate with crawling, indexing, and ML teams to ensure retrieval and ranking requirements are met\n\nEnable safe experimentation with retrieval, ranking, and extraction strategies\n\nYou may be a good fit if you have:\n6+ years of software engineering experience, some of it in search / information retrieval\n\nStrong IRfundamentals: inverted indexes, BM25/TF-IDF, query understanding, ranking, and evaluation (nDCG/MRR/recall@k)\n\nExperience with vector & hybrid retrieval: ANN, dense+sparse fusion, embeddings models\n\nExperience building structured extraction over messy/unstructured domain data\n\nFluent in Python and comfortable with systems-level performance work\n\nStrong candidates may also have experience with:\nKnowledge graphs: entity resolution, entity linking / NER, graph DBs (Neo4j), ontologies / schema design\n\nOwning relevance / ranking for a real product and improving it against IRmetrics\n\nData quality, truth discovery, or systems that decide how much to trust a piece of information\n\nPublished work on IR, ranking, or knowledge 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