Principal Product Manager II, AI/Vectors Elasticsearch

What is The Role

What You Will Be Doing

• Conduct market research to uncover emerging trends in search/vector databases as well as SOTA model research in emerging areas to manage unstructured data, working alongside our model team.
• Evaluate the competitive landscape to enhance product positioning and develop user personas that guide feature decisions.
• Collaborate with cross-functional teams to align the product strategy with business goals while prioritizing features based on customer feedback and market demands.
• Establish long-term product goals and milestones for the vector use cases, document AI and unstructured data management to drive its development. Prioritize features and enhancements based on user feedback and technical feasibility. Allocate resources skillfully to ensure timely updates, while coordinating with engineering teams to validate roadmap assumptions. Communicate updates and expectations clearly to stakeholders and executives.
• Own the vision, strategy, and multi-quarter roadmap for Elastic's vector database as well as core search use cases, from low-level indexing internals to the developer-facing APIs and SDKs.
• Partner deeply with engineering and applied research on trade-offs, be a credible technical peer in those conversations. Define and defend the metrics that matter: recall, latency, index size, ingestion throughput, and total cost of ownership. Advance the competitiveness of Elastic AI and Vector products across all deployments - Serverless and Self-managed.
• Define a unified strategy and roadmap across embedding/reranking models and vector indexing/ semantic ingestion, model lifecycle, and end-to-end retrieval quality.
• Manage the operating cadence. Make sure shared metrics and priorities align the research, cloud, and engineering teams. Address trade-offs where model and index decisions intersect.

What You Bring

• Bachelor's degree in Computer Science, Engineering, or related field
• Master's degree in a relevant discipline preferred
• 8+ years in product management, with significant time on technical infrastructure, databases, search, or ML/AI platforms.
• You should have good working knowledge of vector search fundamentals. This includes embeddings, ANN indexing, similarity metrics, and trade-offs between recall and latency. Additionally, you need to know about hybrid retrieval.
• You should have a working knowledge of generative models, model fine tuning, model capabilities and document processing for large corpus
• You don't need to be a researcher, but you should hold your own with engineers on these topics. A track record of shipping developer- or infrastructure-facing products that were adopted at scale.