Elastic
Enterprise · Search
- HQ
- Mountain View, US
- Founded
- 2012
- Website
- elastic.co
Elastic has 28 active AI-related job listings, with a significant focus on roles related to agents, which constitute 86% of their openings. The majority of these positions are within Engineering. The company is hiring across the United States, United Kingdom, and Canada. Recurring technical tags include vector_db, RAG, and model_serving, suggesting a focus on building and deploying AI applications that leverage external knowledge.
Enterprise · Search
Currently tracking 16 active AI roles, up 55% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $192k–$593k (avg $307k).
Elastic currently has 30 active AI-related roles in our index. The most common open titles are: Senior Software Engineer - Search Relevance in ES|QL - Elasticsearch (6), Principal Software Engineer - Vector Search - Elasticsearch (5), Senior Software Engineer - SSC (4), Principal AI Ecosystem Architect - OpenAI/Anthropic (2), Principal Product Manager Agents and Context - Elasticsearch (2). Most positions are in Engineering and Research.
Elastic's active AI hiring is concentrated in: agents (87%), application (10%), serving infrastructure (3%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Elastic is hiring AI talent in: United States (16 roles), United Kingdom (7 roles), Spain (4 roles), Canada (3 roles).
Job postings at Elastic most frequently mention: Observability, Data Governance, Content Generation, Cloud Security, Cloud Infrastructure.
In the past 30 days, Elastic has posted 23 new AI-related roles. That is a +92% change versus the prior 30 days (12 → 23).
| Title | Stage | AI score |
|---|---|---|
| Senior AI Data Engineer Senior AI Data Engineer responsible for building and maintaining the golden customer dataset, making it AI-ready for downstream AI workflows (account research, lead scoring, churn signals, CSM briefings). This involves designing canonical datasets, implementing enrichment pipelines, deduplication, entity resolution, validation systems, chunking, embedding strategy, metadata design, and source integration. The role also owns quality, lineage, monitoring, drift detection, and documentation for AI consumption. Requires experience with GTM data, preparing data for RAG, embeddings, and AI agents, and using LLMs for data tasks. Experience with Python, SQL, cloud infrastructure, orchestration, and Elastic Stack is necessary. | DataAgent | 7 |