Jobs at Elastic

Manage approximately 50 enterprise accounts across the Greater Toronto Area, acquiring new logos and expanding existing customers. Lead complex, multi-threaded SaaS and cloud sales cycles using MEDDPICC, build executive and technical relationships, leverage cloud and channel partners, articulate business value, negotiate multi-year agreements, and coordinate internal teams to achieve revenue targets and accurate forecasting.

Own net-new revenue and expansion across Canada's AI-native and emerging technology ecosystem. Build territory and outbound strategies, conduct technical discovery, position Elastic's Search AI platform, guide consumption-based cloud sales, maintain forecast accuracy using MEDDPICC, and collaborate with solutions architects, developer relations, hyperscalers, venture capital, and incubator partners. The role targets technical founders, developers, AI engineers, data scientists, and executive technology leaders in Toronto and Montreal.

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 vision, strategy, and roadmap for Identity Threat Detection & Response (ITDR) in Elastic Security. Define detection, investigation, and response for human and non-human identities, collaborate with threat research, data scientists, and engineers, and build identity-focused analyst workflows across SIEM and XDR. Engage customers, evangelize product capabilities, and drive enterprise-grade integrations and automated containment with human oversight.

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.

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.

Own and improve Elastic’s observability infrastructure across hosted cloud deployments. Responsibilities include Terraform-based infrastructure delivery, Python and Go development, production operations, incident response, on-call participation, RCA and postmortem writing, code and design reviews, mentoring, and improving operational documentation and processes. The role also involves operating Linux and containerized workloads, delivering complex projects independently, and maintaining secure, reliable platform infrastructure.

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.

Design, implement, and optimize Elasticsearch core storage and query engine components for logs/metrics workloads. Lead architecture, resolve performance bottlenecks, improve storage efficiency and query execution, mentor engineers, collaborate across distributed teams, triage community issues, and write idiomatic modern Java.