Top Tech Jobs & Startup Jobs in Toronto

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 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.