Principal Product Manager (AI/Vectors)

• We’re looking for a Principal Product Manager to lead the strategy and roadmap for Elastic’s embedding/reranking and vector database and related capabilities. In this senior role, you’ll be the go-to expert on search/vector search, guiding the direction for technologies like vector indexing and hybrid retrieval

• This position offers the chance to shape a vital part of our product, working closely with engineering and research teams to ensure that our offerings meet the needs of numerous organizations while keeping pace with the evolving landscape of AI

• 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

Benefits

• Toast to your health: Fully paid health coverage for you and your family, in many locations.

• Craft your calendar: Flexible location and schedule for most roles.

• Create space for you: Distributed by design workforce, plus generous number of vacation days each year.

• Embrace parenthood: Minimum of 16 weeks of parental leave, plus generous family formation benefits.

• Give back your time: 40 hours each year to use toward volunteering with organizations and causes you’re passionate about.

• Amplify your impact: Double your charitable giving — we match donations up to $1500 USD (or local currency equivalent).- 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

• 8+ years in product management, with significant time on technical infrastructure, databases, search, or ML/AI platforms

• Master’s degree in a relevant discipline preferred

• Bachelor’s degree in Computer Science, Engineering, or related field

• You should have a working knowledge of generative models, model fine tuning, model capabilities and document processing for large corpus

• 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