Lead the strategy and roadmap for Elastic's embedding/reranking and vector database capabilities in a senior product management role.
Posted by employer 1 day ago
First seen on Joblaze 2 hours ago
Last verified on the company career page 2 hours ago
Skills & Technologies
What you'll build
- Conduct market research on search/vector databases
- Evaluate competitive landscape for product positioning
- Collaborate with cross-functional teams on product strategy
- Establish long-term product goals and milestones
- Own the vision and roadmap for Elastic's vector database
Must have
- Bachelor's degree in Computer Science or related field
- 8+ years in product management
- Good working knowledge of vector search fundamentals
- Experience with generative models and document processing
Nice to have
- Master's degree in a relevant discipline
Requirements
- Experience
- 8+ years
- Education
- Bachelor's degree
Not disclosed in this posting: work arrangement, visa sponsorship.
Benefits
Joblaze summary
In this role, the Principal Product Manager will oversee the strategy and roadmap for Elastic's vector database and related capabilities, focusing on search and vector search technologies. The position requires a strong background in product management, particularly in technical infrastructure and AI, along with expertise in vector search fundamentals and hybrid retrieval. This senior role is suited for individuals with extensive experience in shipping developer-facing products and a solid understanding of machine learning concepts. The team collaborates closely with engineering and research to ensure product alignment with market needs.
Joblaze insights
- Listed today — first seen on Joblaze October 23, 2026. Last confirmed on Elastic's careers page October 23, 2026.
- This exact title is also open at 2 other locations at Elastic: Canada, United States.
- Starts above 25% of 40 comparable principal product roles we track (median $179,500 across 22 companies).
Quick facts
- What's the salary range?
- Elastic lists €130,900–€207,100 for this role.
- How much experience is required?
- At least 8 years of relevant experience for this Principal Product Manager II, AI/Vectors - Elasticsearch role.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: AI/ML, Machine Learning, Search, databases, vector search.
- What seniority level is this role?
- Elastic targets principal-level candidates for this position.
- Is this full-time or contract?
- Full-time for this Principal Product Manager II, AI/Vectors - Elasticsearch role at Elastic.
From the original posting
Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI.
What is The Role
What You Will Be Doing
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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.
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Evaluate the competitive landscape to enhance product positioning and develop user personas that guide feature decisions.
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Collaborate with cross-functional teams to align the product strategy with business goals while prioritizing features based on customer feedback and market demands.
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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.
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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.
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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.
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Define a unified strategy and roadmap across embedding/reranking models and vector indexing/ semantic ingestion, model lifecycle, and end-to-end retrieval quality.
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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
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Bachelor's degree in Computer Science, Engineering, or related field
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Master's degree in a relevant discipline preferred
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8+ years in product management, with significant time on technical infrastructure, databases, search, or ML/AI platforms.
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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.
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You should have a working knowledge of generative models, model fine tuning, model capabilities and document processing for large corpus
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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.
Compensation for this role is in the form of base salary. This role does not have a variable compensation component.
At Elastic, our compensation philosophy aims to provide fair, competitive and transparent remuneration. Salary ranges are established based on a combination of external market benchmarks, internal pay equity considerations, and the responsibilities and complexity associated with each role. This approach helps ensure consistency across comparable roles while remaining competitive within the relevant labour markets.
The final compensation offered within the applicable range will be determined based on several objective factors, including relevant professional experience, level of skills and expertise, alignment with the role requirements, and the overall scope and complexity of the position.
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