The opportunity
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…
What you'll do
Elasticsearch Core Architecture: Translate highly complex business requirements into resilient, next-generation enterprise retrieval architectures built natively on distributed Elasticsearch environments.
Cluster Governance & Design: Lead the overarching technical strategy and design authority for high-stakes customer engagements—from initial node blueprinting and capacity planning to custom mappings, shard strategy, Index Lifecycle Management (ILM), and cross-cluster replication (CCR/CCS).
Advanced Vector Search & AI Engineering: Design and operationalize cutting-edge semantic search architectures utilizing Elasticsearch’s native vector database capabilities, including kNN, Approximate Nearest Neighbor (ANN), ELSER (Elastic Learned Sparse Encoder), hybrid retrieval, and Retrieval-Augmented Generation (RAG) pipelines.
Performance Tuning & Optimization: Profile, benchmark, and tune distributed search and indexing performance for ultra-high-QPS environments with aggressive sub-second SLAs, optimizing Apache Lucene segment merging, caching layers, and heap/garbage collection configurations.
High-Throughput Indexing: Architect robust distributed ingestion strategies handling petabyte-scale throughput, optimizing cluster state performance, thread pools, and bulk indexing requests for maximum efficiency.
Cross-Functional Influence: Collaborate cross-functionally with Elastic Product Management and Core Engineering to influence the Elasticsearch codebase roadmap, surface edge-case bugs, and drive feature enhancement requests based on enterprise field realities.
What they're looking for
- Community & IP Development: Capture, formalize, and publish global best practices, reference architectures, and reusable solution patterns across the broader Elasticsearch and open-source engineering communities.
- Culture of Excellence: Drive internal enablement initiatives, mentor senior engineers, and cultivate a culture of continuous technical excellence and deep mastery of Elasticsearch internals.
- + years as a Principal Architect, Lead Engineer, or Senior Systems Consultant: with recognized, deep technical expertise specifically focused on Elasticsearch at a massive scale.
- Elasticsearch Internals Mastery: Comprehensive understanding of distributed systems theory as it applies to Elasticsearch, including consensus protocols, internal node roles (master, data, ingest, machine learning), Apache Lucene indexing mechanics, and cluster state management.
