Source description
About the role
Own the product strategy and roadmap for Data Search & AI - what we build, why, and how it ladders up to the company's agentic search bet.
Lead the productisation of semantic search and agentic search over our data: deciding what queries we support, what "good" answers look like, and how AI capabilities are exposed to customers.
Shape the AI stack at a product level - embedding models, retrieval strategies, ranking, reranking, agent design - making the tradeoff calls between relevance, latency, cost, and quality.
Own evaluation: design the eval sets and metrics that determine whether the search is actually good, and use them to drive iteration.
Decide when models are good enough to ship and when they need more work - coverage vs precision tradeoffs, hallucination tolerance, when to fall back to deterministic approaches.
Drive customer discovery on how customers actually want to query our data - what they ask, what frustrates them today, what an AI-native interface should let them do that a traditional API or UI cannot.
Partner closely with the Data Product and Platform squads on what data is being indexed and what new datasets unlock new search capabilities.
Work with stakeholders outside Product - Sales, Customer Success, Engineering leadership - to keep the squad connected to commercial and operational reality.
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