Jobs at Elastic

Build and enhance user interfaces for Elastic Fleet and Streams using TypeScript, React, and Node.js. Own complex projects end-to-end, lead architecture and system-design decisions, establish coding standards, conduct code reviews, and implement automated testing and deployment. Collaborate asynchronously with designers, product managers, and distributed engineers to deliver scalable, accessible, user-focused experiences for observability, log ingestion, and real-time data workflows.

Lead the technical vision and roadmap for CI/CD, release, and platform engineering productivity. Identify and resolve infrastructure bottlenecks and supply-chain security risks, establish scalable release gates, direct engineers through complex cross-organizational projects, and serve as incident commander for critical release incidents. The role also involves technical communication, architecture documentation, code and design reviews, mentorship, prototyping, and production debugging.

Develop and optimize Elasticsearch performance across distributed and serverless architectures. Build performance models, profiling and benchmarking tools, regression detection systems, and AI-assisted optimization harnesses. Analyze bottlenecks in logging, metrics, vector search, and ES|QL while implementing high-performance, thread-safe Java code. Collaborate across teams, provide technical guidance, and improve scalability and predictability of Elasticsearch.

Develop and maintain Elasticsearch’s Inference API and plugin framework, integrating internal and external AI inference providers. Responsibilities include API and protocol design, streaming, asynchronous execution, rate limiting, configuration, billing integration, error handling, open-source contribution reviews, incident response, and comprehensive unit, REST, and rolling-upgrade testing. The role requires strong Java expertise, low-level concurrency knowledge, API design skills, and collaboration across distributed teams.

Own vision, strategy, and roadmap for Identity Threat Detection & Response within Elastic Security. Define detections, investigations, and automated responses for human and non-human identities; collaborate with threat research, data scientists, and engineers; integrate identity signals with endpoint, cloud, and network data; engage customers and evangelize product capabilities.

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.

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.

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.