{"id":2243418,"url":"https://alion.io/job/nebius-senior-ml-engineer-search-optimization-agentic-search","title":"Senior ML Engineer, Search Optimization (Agentic Search)","company":{"id":6364,"name":"Nebius","domain":"nebius.com","url":"https://alion.io/company/nebius","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":75,"open_postings":135,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-12T06:55:09Z"}},"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":{"min_usd":127000,"max_usd":322000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1986},"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":"2026-06-29T08:49:31Z","employer_posted_date":"2026-10-08","last_verified_at":"2026-10-13T04:41:48Z","board_verified":true,"closed_at":null,"days_open":105,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":104},"description":"About Nebius:\nNebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure.\nBuilt by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.\nListed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D.\nThe 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 graphs\n\nBenefits & Perks:\nCompetitive compensation\nCareer growth and learning opportunities\nFlexibility and ownership\nCollaborative and innovative culture\nOpportunity to work on impactful AI projects\nInternational environment and talented teams\nWhat's it like to work at Nebius:\nFast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI \nEqual Opportunity Statement:\nNebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. 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