Jobs at Elastic

Develop and maintain foundational Elasticsearch infrastructure, APIs, shared services, scripting, observability, and cloud orchestration. Design horizontally scalable features, resolve complex performance and concurrency issues, improve automation and developer productivity, and lead cross-team engineering projects. The role requires strong Java expertise, ownership of operational investigations, collaboration across distributed teams, and effective use of AI in development and debugging.

Design, build, scale, and operate Elastic’s multi-cloud platform infrastructure. Develop software, tooling, and automation for reliability, Kubernetes operations, and SaaS services. Lead initiatives that improve system engineering, incident response, alerting, problem management, and customer impact prevention. Collaborate across distributed teams, participate in follow-the-sun on-call rotations, and mentor colleagues while driving operational excellence.

Own the production improvement loop for Elastic’s Context Engine, improving AI agents, automations, retrieval, memory, telemetry, and evaluation. Design safe rollout processes, regression coverage, experiments, and quality, latency, and cost evaluators. Build production code in TypeScript or Python, collaborate with data science and product teams, mentor engineers, review designs, and influence the roadmap for agentic behavior and enterprise AI products.

Lead the technical vision and development of Elasticsearch’s core search infrastructure, including indexing, retrieval, query execution, and relevance. Research search algorithms and data structures, improve Lucene and Elasticsearch capabilities, guide technical roadmaps, mentor expert engineers, and collaborate across distributed teams. The role requires hands-on development of scalable, high-performance distributed search systems and autonomous leadership from design through implementation.

Lead Elasticsearch vector search initiatives, designing and implementing search features, algorithms, and data structures while resolving complex bugs. Improve vector similarity search performance, relevance, and maintainability in modern Java. Collaborate with distributed engineering and open-source communities, guide cross-functional projects, review code, and provide technical leadership across codebases and teams.

Lead development of Elasticsearch vector similarity search capabilities, including designing and implementing new features, improving existing functionality, resolving bugs, and developing algorithms or data structures. Serve as a subject-matter expert, collaborate with a globally distributed engineering and open-source community, review code, guide cross-functional initiatives, and build scalable, idiomatic Java software for high-performance search.

Lead development of Elasticsearch search relevance and vector search capabilities. Design and implement new search features, algorithms, and data structures; fix complex bugs; improve performance and maintainability; and contribute to technical direction. Collaborate asynchronously with globally distributed engineers and open-source contributors, write modern Java, review code, and guide initiatives from design through delivery.

Lead development of Elasticsearch search and vector-search capabilities, designing algorithms and data structures, implementing Java features, fixing complex bugs, improving relevance and performance, and contributing to technical direction. Collaborate asynchronously with globally distributed engineers and open-source contributors, review code, triage issues, and drive initiatives from design through completion.

Lead architectural and code-level performance engineering for Elasticsearch, from design through production. Develop performance models, profile distributed systems, identify bottlenecks, optimize stateful and serverless architectures, and build benchmarking and regression-detection automation. Drive AI-assisted optimization tooling, collaborate across teams, and mentor engineers. Focus areas include Java and JVM performance, concurrency, distributed systems, logging, metrics, vector search, ES|QL, storage engines, and scalable search infrastructure.

Build and evolve the Elastic MCP server and tooling (primarily in Go), create a portable platform for agent skills, design third-party AI ecosystem integrations, enhance the Elastic CLI, and develop/maintain libraries and integrations (including Rust). Drive correctness via evals, collaborate with product and external partners, and prioritize developer experience and product thinking across fast-changing, ambiguous work.

Design and improve Elasticsearch distributed-systems components for consistent, concurrent indexing and cluster coordination. Scale shards, nodes, and petabyte datasets, diagnose performance and concurrency issues, support operations/support teams, and own end-to-end projects including technical design and cross-team development.

Design, implement, and optimize Elasticsearch core storage and query engine for observability workloads. Lead architecture, resolve performance bottlenecks, improve storage/indexing/query execution, mentor engineers, drive cross-team initiatives, collaborate with global community, and write idiomatic modern Java.

Design, implement, and scale Elasticsearch distributed systems: improve concurrent indexing, maintain cluster coordination, optimize performance at petabyte scale, debug concurrency and performance issues, and lead cross-engineer projects.

Lead product development for logs and logs analytics within Elastic Observability. Drive end-to-end log collection, ingest, management, and features for SREs (GenAI integration, automated SLO/SLI, intelligent alerting, correlation of logs/metrics/traces). Collaborate with engineering, UX, sales, and marketing to prioritize roadmap, track engagement, and drive adoption and go-to-market activities.

Full‑stack engineer on the self‑service cloud growth team building onboarding and trial experiences. Design and implement scalable frontend and backend features, APIs, agentic interfaces, and data evolution strategies. Collaborate with PMs, designers, and solution teams to integrate cloud and Kibana flows and operationalize software in production.