The Product
In 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.
We 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.
The Role
You 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.
This 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.
In this position, your responsibility will be to
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Design, implement, and operate the retrieval system for a search vertical
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Connect and tune the data pipeline, from ingestion to relevance tuning
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Build knowledge-graph and entity-resolution layers: entity linking / NER, ontologies, and graph databases (Neo4j or similar)
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Develop structured-extraction pipelines over messy, unstructured domain data
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Reason about freshness and trust: model how confident we are in a fact and how stale it has become before we serve it
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Define evaluation and quality metrics for relevance and drive measurable improvements
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Collaborate with crawling, indexing, and ML teams to ensure retrieval and ranking requirements are met
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Enable safe experimentation with retrieval, ranking, and extraction strategies
You may be a good fit if you have:
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6+ years of software engineering experience, some of it in search / information retrieval
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Strong IR fundamentals: inverted indexes, BM25/TF-IDF, query understanding, ranking, and evaluation (nDCG/MRR/recall@k)
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Experience with vector & hybrid retrieval: ANN, dense+sparse fusion, embeddings models
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Experience building structured extraction over messy/unstructured domain data
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Fluent in Python and comfortable with systems-level performance work
Strong candidates may also have experience with:
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Knowledge graphs: entity resolution, entity linking / NER, graph DBs (Neo4j), ontologies / schema design
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Owning relevance / ranking for a real product and improving it against IR metrics
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Data quality, truth discovery, or systems that decide how much to trust a piece of information
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Published work on IR, ranking, or knowledge graphs