Top Tech Jobs & Startup Jobs in Toronto

Define and drive the Enterprise Intelligence product strategy and roadmap. Conduct customer research, identify enterprise intelligence use cases, and build actionable workflows, dashboards, recommendations, and proactive experiences. Partner with engineering, design, data, go-to-market, and customer-facing teams to translate ambiguous concepts into detailed product experiences with clear user flows, quality standards, and measurable outcomes. Prioritize initiatives, support customer adoption, and establish product foundations for a new enterprise intelligence category.

Own the product strategy and roadmap for Glean Model Hub. Evaluate LLMs, manage model and inference-provider relationships, support customer experimentation, and oversee usage, cost, and capacity planning. Partner closely with customers and cross-functional teams to prioritize capabilities, enable adoption of new models, improve AI workflows, and build scalable processes. The role requires strong technical product management experience spanning enterprise SaaS, AI/ML operations, APIs, UX, and third-party partnerships.

Build and ship fully native iOS and Android applications using Swift/SwiftUI and Kotlin/Jetpack Compose. Own mobile architecture, CI/CD, release infrastructure, enterprise authentication, managed-device support, security controls, OS integrations, and auditability. Deliver features including search, AI assistants, agents, notifications, widgets, and voice modes while ensuring accessibility, performance, offline behavior, and enterprise readiness. Establish AI-assisted development workflows and represent mobile in security reviews.

Improve the quality and reliability of Glean’s AI assistants and agents through evaluation-driven development, model routing, retrieval, prompting, and post-training. Build supervised and preference-optimized models, reinforcement-learning environments, evalsets, LLM judges, and agentic verifiers. Analyze production traces, calibrate evaluation signals against human graders, balance quality with latency and cost, and partner with product, platform, research, and customer teams to ship production ML improvements.

Build data and evaluation systems for Glean’s AI products, including benchmark datasets, scalable eval infrastructure, agent observability, LLM-based verifiers, CI and production evaluation pipelines, and enterprise simulation environments. Analyze traces and failures, measure quality with statistical rigor, and translate findings into product improvements. Partner with research, engineering, product, and go-to-market teams to improve agent performance while balancing quality, latency, and cost.