Top Tech Jobs & Startup Jobs in Montreal

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.