Principal Product Manager II, AI/Vectors - Elasticsearch

Elastic
Full-time•Product / Owner•Ireland · Remote within Ireland · Hybrid
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At a glance

Employment type
Full-time
Workplace
Hybrid

Technologies & skills

Other technical skills

What you'll be doing

Principal Product Manager II, AI/Vectors at Elastic leads strategy and roadmap for embedding/reranking, vector database, and related capabilities. The role focuses on search/vector search, collaborating with engineering and research teams to deliver advanced AI-driven search solutions for diverse organizations.

  • Conduct market research on search/vector databases and SOTA model research for unstructured data
  • Evaluate competitive landscape and develop user personas to guide feature decisions
  • Collaborate with cross-functional teams to align product strategy with business goals
  • Establish long-term product goals and milestones for vector use cases and AI data management
  • Prioritize features and enhancements based on user feedback and technical feasibility
  • Allocate resources and coordinate with engineering teams to validate roadmap assumptions
  • Own vision, strategy, and roadmap for Elastic's vector database and core search use cases
  • Partner with engineering and research on technical trade-offs and define key metrics

Key requirements

Must-have

  • Bachelor's degree in Computer Science, Engineering, or related field
  • 8+ years in product management with focus on technical infrastructure, databases, search, or ML/AI platforms
  • Working knowledge of vector search fundamentals including embeddings, ANN indexing, similarity metrics, recall/latency trade-offs, and hybrid retrieval
  • Working knowledge of generative models, model fine tuning, model capabilities, and document processing for large corpus
  • Track record of shipping developer- or infrastructure-facing products adopted at scale

Nice-to-have

  • Master's degree in a relevant discipline

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

Role signals

Technical focus
product management for AI/vector search and database technologies
Hands-on vs management
Hands-on

Full job description

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

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.

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.

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.

The typical starting salary range for this role is:

€130.900—€207.100 EUR

Additional Information - We Take Care of Our People

As a distributed company, diversity drives our identity. Whether you’re looking to launch a new career or grow an existing one, Elastic is the type of company where you can balance great work with great life. Your age is only a number. It doesn’t matter if you’re just out of college or your children are; we need you for what you can do.

We strive to have parity of benefits across regions and while regulations differ from place to place, we believe taking care of our people is the right thing to do.

  • Competitive pay based on the work you do here and not your previous salary

  • Health coverage for you and your family in many locations

  • Ability to craft your calendar with flexible locations and schedules for many roles

  • Generous number of vacation days each year

  • Increase your impact - We match up to $2000 (or local currency equivalent) for financial donations and service

  • Up to 40 hours each year to use toward volunteer projects you love

  • Embracing parenthood with minimum of 16 weeks of parental leave

Different people approach problems differently. We need that. Elastic is an equal opportunity employer and is committed to creating an inclusive culture that celebrates different perspectives, experiences, and backgrounds. Qualified applicants will receive consideration for employment without regard to race, ethnicity, color, religion, sex, pregnancy, sexual orientation, gender perception or identity, national origin, age, marital status, protected veteran status, disability status, or any other basis protected by federal, state or local law, ordinance or regulation.

We welcome individuals with disabilities and strive to create an accessible and inclusive experience for all individuals. To request an accommodation during the application or the recruiting process, please email candidate_accessibility@elastic.co. We will reply to your request within 24 business hours of submission.

Applicants have rights under Federal Employment Laws, view posters linked below: Family and Medical Leave Act (FMLA) Poster; Pay Transparency Nondiscrimination Provision Poster; Employee Polygraph Protection Act (EPPA) Poster and Know Your Rights (Poster)

Elasticsearch develops and distributes technology and information that is subject to U.S. and other countries’ export controls and licensing requirements for individuals who are located in or are nationals of the following sanctioned countries and regions: Belarus, Cuba, Iran, North Korea, Syria, or Russia, including the Ukrainian territories annexed by Russia (The Crimea region of Ukraine, The Donetsk People's Republic (DNR), The Luhansk People's Republic (LNR), Kherson or Zaporizhzhia). If you are located in or are a national of one of the listed countries or regions, an export license may be required as a condition of your employment in this role. Please note that national origin and/or nationality do not affect eligibility for employment with Elastic.

Please see here for our Privacy Statement.

Interview prep pack

Grounded in this listing. Use it to prepare examples before you apply.

Your interview focus

Based on this listing, the Principal Product Manager II, AI/Vectors - Elasticsearch role centers on leading the strategy, roadmap, and execution for Elastic's vector database and related AI-driven search capabilities, with a strong emphasis on technical depth in vector search, embeddings, and collaboration with engineering and research teams.

  • Technical depth in vector search and AI infrastructure·High
  • Product strategy and roadmap ownership·High
  • Cross-functional collaboration with engineering/research·High

Only have 30 minutes?

Follow a focused preparation plan based on this job.

Start 30-minute prep

Your 30-minute plan

  1. Review Elastic's AI and Vector Product Materials

    0–8 min

    Study Elastic's official documentation, product pages, and recent announcements on vector search and AI features to understand current capabilities and positioning.

  2. Research Vector Search and Hybrid Retrieval Trends

    8–15 min

    Read recent articles, papers, and competitor product updates on vector search, embeddings, and hybrid retrieval to be conversant in state-of-the-art approaches.

  3. Prepare Examples of Technical Product Leadership

    15–22 min

    Select and outline 2-3 stories from your experience where you led technical product strategy, made trade-offs, and shipped developer/infrastructure products at scale.

  4. Review and Practice Discussing Key Metrics

    22–26 min

    Be ready to explain how you define, track, and use metrics like recall, latency, and TCO to drive product decisions and communicate with stakeholders.

  5. Draft Role-Specific Questions

    26–30 min

    Prepare thoughtful questions about Elastic's product strategy, technical challenges, and team dynamics to demonstrate your engagement and fit.

Likely questions

, 6 items

Priority reflects how strongly this topic is emphasised in the job listing, not whether it will be asked.

Talking points

, 6 items
  • Vector Search Fundamentals and Trade-offs

    Demonstrate your understanding of embeddings, ANN indexing, similarity metrics, and balancing recall and latency, as these are core to the product's technical direction.

  • Product Strategy and Roadmap Ownership

    Showcase your experience in setting vision, defining multi-quarter roadmaps, and aligning product goals with business objectives for technical products.

  • Collaboration with Engineering and Research

    Prepare examples of working closely with technical teams, making trade-offs, and being a credible technical peer in discussions about AI, search, and infrastructure.

  • Market Research and Competitive Analysis

    Highlight your ability to analyze market trends, evaluate competitors, and translate findings into actionable product decisions and user personas.

  • Metrics Definition and Outcome Measurement

    Be ready to discuss how you define, track, and defend key metrics (e.g., recall, latency, index size, throughput, TCO) to measure product success.

  • Shipping Scalable Developer/Infrastructure Products

    Provide examples of products you have launched that were adopted at scale, especially those targeting developers or technical users.

What to research

, 4 items
  • Elastic's Vector Database and AI Product Offerings

    Review Elastic's documentation, blog posts, and product pages related to vector search, embeddings, and AI capabilities.

  • State-of-the-Art in Vector Search and Hybrid Retrieval

    Research current trends, leading models, and best practices in vector search, ANN indexing, and hybrid retrieval approaches.

  • Competitive Landscape in AI-Powered Search

    Identify key competitors and analyze their vector search and AI product strategies to understand Elastic's positioning.

  • Metrics for Vector Database Performance

    Prepare to discuss how to define, measure, and optimize metrics such as recall, latency, index size, throughput, and TCO.

Questions to ask

, 6 items
  1. How does Elastic prioritize between advancing core search capabilities and expanding AI/vector features?

    Why ask this? Clarifies the company's strategic focus and how product priorities are set.

  2. What are the biggest technical challenges currently facing the vector database team?

    Why ask this? Gains insight into the team's pain points and where your expertise could add value.

  3. How does the product team collaborate with engineering and research to make trade-off decisions?

    Why ask this? Assesses the cross-functional dynamics and decision-making processes.

  4. What metrics are most important for measuring the success of Elastic's vector search products?

    Why ask this? Shows your focus on outcomes and helps you understand expectations for the role.

  5. How does Elastic gather and incorporate customer feedback into the product roadmap for AI and vector features?

    Why ask this? Reveals the feedback loop and customer-centricity of the product process.

  6. What is the vision for Elastic's AI and vector search capabilities over the next few years?

    Why ask this? Helps you understand long-term goals and how your role would contribute.

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Apply NowApply before: 21 Nov 2026