Senior Software Engineer, Vertical Search (Agentic Search) - Nebius

Design, implement, and operate the retrieval system for a search vertical Connect and tune the data pipeline, from ingestion to relevance tuning Build knowledge-graph and entity-resolution layers: entity linking / NER , ontologies, and graph databases (Neo4j or similar) Develop structured-extraction pipelines over messy, unstructured domain data Reason about freshness and trust: model how confident we are in a fact and how stale it has become before we serve it Define evaluation and quality metrics for relevance and drive measurable improvements Collaborate with crawling, indexing, and ML teams to ensure retrieval and ranking requirements are met Enable safe experimentation with retrieval, ranking, and extraction strategies 6+ years of software engineering experience, some of it in search / information retrieval Strong IR fundamentals: inverted indexes, BM25 / TF-IDF , query understanding, ranking, and evaluation (nDCG/ MRR /recall@k) Experience with vector & hybrid retrieval: ANN, dense+sparse fusion, embeddings models Experience building structured extraction over messy/unstructured domain data Fluent in Python and comfortable with systems-level performance work Knowledge graphs: entity resolution, entity linking / NER , graph DBs (Neo4j), ontologies / schema design Owning relevance / ranking for a real product and improving it against IR metrics Data quality, truth discovery, or systems that decide how much to trust a piece of information Published work on IR , ranking, or knowledge graphs Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams

Design, implement, and operate the retrieval system for a search vertical Connect and tune the data pipeline, from ingestion to relevance tuning Build knowledge-graph and entity-resolution layers: entity linking / NER , ontologies, and graph databases (Neo4j or similar) Develop structured-extraction pipelines over messy, unstructured domain data Reason about freshness and trust: model how confident we are in a fact and how stale it has become before we serve it Define evaluation and quality metrics for relevance and drive measurable improvements Collaborate with crawling, indexing, and ML teams to ensure retrieval and ranking requirements are met Enable safe experimentation with retrieval, ranking, and extraction strategies 6+ years of software engineering experience, some of it in search / information retrieval Strong IR fundamentals: inverted indexes, BM25 / TF-IDF , query understanding, ranking, and evaluation (nDCG/ MRR /recall@k) Experience with vector & hybrid retrieval: ANN, dense+sparse fusion, embeddings models Experience building structured extraction over messy/unstructured domain data Fluent in Python and comfortable with systems-level performance work Knowledge graphs: entity resolution, entity linking / NER , graph DBs (Neo4j), ontologies / schema design Owning relevance / ranking for a real product and improving it against IR metrics Data quality, truth discovery, or systems that decide how much to trust a piece of information Published work on IR , ranking, or knowledge graphs Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams

Design, implement, and operate the retrieval system for a search vertical Connect and tune the data pipeline, from ingestion to relevance tuning Build knowledge-graph and entity-resolution layers: entity linking / NER , ontologies, and graph databases (Neo4j or similar) Develop structured-extraction pipelines over messy, unstructured domain data Reason about freshness and trust: model how confident we are in a fact and how stale it has become before we serve it Define evaluation and quality metrics for relevance and drive measurable improvements Collaborate with crawling, indexing, and ML teams to ensure retrieval and ranking requirements are met Enable safe experimentation with retrieval, ranking, and extraction strategies 6+ years of software engineering experience, some of it in search / information retrieval Strong IR fundamentals: inverted indexes, BM25 / TF-IDF , query understanding, ranking, and evaluation (nDCG/ MRR /recall@k) Experience with vector & hybrid retrieval: ANN, dense+sparse fusion, embeddings models Experience building structured extraction over messy/unstructured domain data Fluent in Python and comfortable with systems-level performance work Knowledge graphs: entity resolution, entity linking / NER , graph DBs (Neo4j), ontologies / schema design Owning relevance / ranking for a real product and improving it against IR metrics Data quality, truth discovery, or systems that decide how much to trust a piece of information Published work on IR , ranking, or knowledge graphs Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams