Drimva

Elastic

Principal Product Manager II, AI/Vectors - Elasticsearch

Ireland +1 · Principal · Full-time · Posted Oct 21, 2026

Location
Ireland
United States
Experience
8+ years in product management; significant time on technical infrastructure, databases, search, or ML/AI platforms.
Education
Bachelor's degree in Computer Science, Engineering, or related field; Master's degree in a relevant discipline preferred.
Visa sponsorship
For individuals located in or nationals of listed sanctioned countries or regions, an export license may be required as a condition of employment.

About the job

Leads the strategy and roadmap for Elastic’s vector database, embedding and reranking models, and search capabilities, partnering with engineering and research to improve retrieval products.

What you'll do

  • Own the multi-quarter product vision and roadmap for vector database and core search use cases, from indexing to developer APIs and SDKs.
  • Research market trends, competitors, customer needs, and emerging models to inform product positioning and feature priorities.
  • Align research, cloud, and engineering teams on priorities, metrics, trade-offs, and delivery across deployments.

What they ask for

  • Bachelor's degree in Computer Science, Engineering, or a related field.
  • 8+ years in product management, including significant time 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 shipping developer- or infrastructure-facing products adopted at scale.

Nice to have

  • Master's degree in a relevant discipline.

Pay

€130.900 — €207.100 EUR (base salary)

Tools

Elasticsearch, vector indexing, hybrid retrieval, embeddings, ANN indexing, generative models, developer-facing APIs, SDKs

AI focus

Leads product strategy for embeddings, reranking, vector indexing, semantic ingestion, model lifecycle, and end-to-end retrieval quality.

Model-written summary · the original posting is authoritative

First seen Oct 27, 2026 · Last verified Nov 6, 2026 (UTC)