Top Tech Jobs & Startup Jobs in Montreal

Drive adoption of Elastic’s AI-powered search solutions among mid-market accounts in Montreal or Quebec City. Build new and existing customer relationships, develop use cases, position open-source and commercial offerings, navigate complex SaaS sales cycles, and exceed territory quotas. Collaborate across business functions, leverage customer and partner ecosystems, and maintain accurate Salesforce forecasts. Fluency in French and experience selling SaaS subscriptions, enterprise search, analytics, security, APM, or cloud solutions are required.

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 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.