Jobs at Elastic

Recruit candidates for G&A and Marketing teams while managing the full hiring lifecycle. Build talent pipelines, advise hiring managers and executives, improve candidate experience, implement recruiting strategies, and represent Elastic’s employer brand. The role requires balancing multiple requisitions, partnering with stakeholders, communicating the company value proposition, and using ATS and recruiting technologies.

Lead a globally distributed endpoint security engineering team developing visibility, prevention, and machine learning capabilities across Windows, macOS, and Linux. Own the technical roadmap, delivery, cross-platform protection parity, and machine learning direction. Mentor senior engineers, manage hiring and career development, collaborate with product and engineering teams, maintain platform vendor relationships, and represent the team through customer engagement, conference presentations, and technical research.

Own the end-to-end improvement loop for Elastic’s Context Engine, including agent behavior, evaluation, telemetry, retrieval, memory, and safe production rollouts. Build production services in TypeScript or Python, design golden datasets and quality gates, analyze traces and customer feedback, and improve agentic systems through experimentation. Collaborate with data scientists, backend engineers, product, and UX while mentoring engineers and shaping technical strategy.

Coordinates marketing budgets, forecasts, accruals, compliance, team culture programs, all-hands meetings, special projects, executive communications, business reviews, and operating processes. Tracks timelines, dependencies, risks, approvals, and follow-ups while supporting global marketing leaders. Requires strong organization, communication, discretion, financial comfort, relationship-building, and project coordination skills. The role is remote within Canada and supports a distributed B2B SaaS marketing organization.

Own the strategy, roadmap, and integrations framework for Elastic’s Streams telemetry ingestion platform. Lead a distributed product management team, drive adoption KPIs, partner across Observability, Security, and Search, and make data ingestion self-service. Collaborate with customers, engineers, designers, sales, marketing, and partners to deliver shared platform capabilities, compelling features, and clear product positioning.

Design, develop, debug, and improve Elastic’s cloud control plane and hosted offerings. Provide technical leadership, guide cross-team architecture, establish development best practices, create technical plans, prioritize features and fixes, and collaborate globally with Support and Elasticsearch engineering teams to deliver reliable, scalable SaaS foundations.

Lead competitive benchmarking for Elasticsearch across search, vector retrieval, analytical, and observability workloads. Build automated benchmark harnesses, datasets, query sets, and reproducible test environments; design fair comparisons; analyze latency, throughput, resource usage, cost, and retrieval quality; investigate performance differences; and collaborate with engineering, product, and content teams to drive improvements and publish technical findings.

Lead the strategy, vision, and multi-quarter roadmap for Elastic’s vector database, embedding and reranking capabilities, and core search use cases. Conduct market and model research, define product metrics, evaluate competition, prioritize features, and coordinate engineering, cloud, research, and business teams. Own technical trade-offs involving vector indexing, retrieval quality, latency, recall, ingestion throughput, and cost while guiding developer-facing APIs and infrastructure products across serverless and self-managed deployments.

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, contribute to Lucene, guide long-term roadmaps, mentor engineers, and collaborate across distributed teams. Build and optimize high-performance distributed search systems while serving as a technical authority in key Elasticsearch domains.

Build and enhance Elasticsearch’s analytical query engine and ES|QL capabilities. Design data-intensive features, algorithms, and data structures; optimize performance; resolve bugs; and work near operating system and hardware layers. Collaborate with globally distributed engineering teams and open-source contributors, guide projects autonomously, review code, and improve Elasticsearch expertise and usability.

Lead the technical vision, roadmap, and development of Elasticsearch’s ES|QL analytics and compute engine. Build data-intensive enhancements using modern Java, research algorithms and data structures, collaborate across teams, mentor engineers, guide technical projects, and improve search and analytics capabilities. Work autonomously in a globally distributed environment while partnering with product, user experience, and customer-facing teams.

Leads competitive intelligence for Elastic’s Search-focused Product Marketing organization. Responsibilities include analyzing competitors’ products, pricing, packaging, strategies, and market developments; creating battlecards, enablement materials, blogs, and web content; supporting win/loss analysis; monitoring search-market trends; and partnering with product marketing, product management, sales, engineering, and leadership to develop competitive positioning.

Lead development of Elasticsearch vector similarity search capabilities, including designing algorithms and data structures, implementing new features, fixing complex bugs, and improving search relevance, performance, and code quality. Collaborate autonomously with a globally distributed engineering team and open-source community, contribute to Lucene, guide cross-functional initiatives, review code, and use modern Java to build scalable search software.

Lead development of Elasticsearch vector similarity search capabilities, including designing algorithms and data structures, implementing features, fixing bugs, improving performance and relevance, and contributing to Lucene. Collaborate asynchronously with globally distributed engineers and open-source contributors, provide technical leadership, review code, and guide cross-functional initiatives. Develop modern Java software and serve as the subject-matter expert for vector search.

Lead Elasticsearch search relevance and vector search initiatives. Design and implement search features, algorithms, and data structures; improve speed, relevance, and scalability; resolve complex bugs; and contribute to Lucene. Collaborate asynchronously with globally distributed engineers and open-source contributors, review code, guide technical direction, and write modern Java. Apply vector similarity techniques and algorithms such as HNSW or IVF on search platforms at scale.

Lead development of Elasticsearch search and vector-search capabilities, including designing algorithms and data structures, implementing new features, improving relevance and performance, fixing complex bugs, and contributing to Lucene. Collaborate autonomously with globally distributed engineers and open-source contributors, guide technical initiatives, review code, and use modern Java to build scalable search functionality.

Lead product strategy and roadmap for Elastic's Control Plane (orchestration and management) for Elastic Cloud. Partner with engineering, sales, and marketing to prioritize features, gather customer and market insights, and drive go-to-market. Focus on scalable orchestration, management, and developer-focused infrastructure capabilities while working asynchronously across distributed teams.

Develop and optimize Elasticsearch performance across distributed and serverless architectures. Build performance models, profiling and benchmarking workflows, regression detection, and AI-assisted optimization harnesses. Analyze bottlenecks in logging, metrics, vector search, and ES|QL; implement scalable, thread-safe, high-performance Java code; and collaborate across teams through technical reviews and guidance. Experience with JVM internals, distributed systems, performance engineering, and benchmarking is required.

Lead architectural and code-level performance engineering for Elasticsearch. Own major optimization initiatives, develop performance models, profile distributed systems, identify bottlenecks, build benchmarks and regression detection, and improve scalability across stateful and serverless architectures. Develop AI-assisted optimization and benchmarking harnesses, collaborate across teams, and mentor engineers. Work includes Java and JVM optimization, concurrency, distributed systems, vector search, storage engines, and native library integration.

Design, implement, and maintain Elasticsearch distributed-systems components to ensure scalable, performant, and resilient indexing, coordination, replication, and cluster communication. Diagnose and fix concurrency and performance issues, push scalability limits, and lead projects end-to-end while supporting support engineers and mentoring engineers across multi-node deployments.