Top Tech Jobs & Startup Jobs in Montreal

Drive independent, high-impact research on frontier AI topics, publish findings, collaborate widely, and mentor new researchers in the field.

Lead end-to-end technical programs for ML infrastructure (inference, serving, efficiency, endpoints). Coordinate cross-functional teams, prioritize overlapping projects, manage timelines, budgets and scope, drive incident management and continuous improvement, and communicate status to engineering and leadership.

As a Software Engineering Intern, you will contribute to building features for user-facing products, APIs, and scalable services for machine learning. Responsibilities include crafting code, designing data pipelines, implementing tools, and enhancing technical documentation.

The engineer will develop features for an AI workspace platform, deploy AI solutions in public sector environments, and interact with clients.

Lead NetSuite ERP architecture, configuration, integrations, and roadmap. Improve Order-to-Cash and Procure-to-Pay processes, implement automation (including AI/LLM agents), enforce data governance and SOX-ready controls, partner with cross-functional teams, manage upgrades and third-party integrations, and provide documentation, training, and user support to ensure financial system stability and accuracy.

Lead month-end close, manage general ledger, reconciliations, and financial statement preparation. Oversee audits and internal controls, drive system implementations, process improvements, and acquisition integrations. Partner cross-functionally, leverage technology to improve accounting efficiency, and provide financial analysis and recommendations to support strategic decisions.

Lead and manage the enterprise SOX control framework: scope, risk assessment, control design, documentation, testing, deficiency remediation, and auditor liaison. Partner cross-functionally to embed controls in transformations and drive automation and efficiency across the program.

Build and ship full‑stack features for North, an enterprise AI workspace. Develop secure autonomous agents, execution engines, integrations, observability and evaluation systems. Collaborate with researchers to productionize models and deliver performant, low‑resource deployment code while adhering to strict security and privacy constraints.

Design, build, and maintain automation and testing infrastructure for the North platform: CI/CD pipelines, GitHub workflows, containerized test environments (Docker/Kubernetes), Helm and ArgoCD deployments, infrastructure-as-code, benchmarking, parallel test execution, and cross-team standards to improve test coverage, reliability, and deployment confidence.

Build and scale ML-optimized HPC infrastructure, manage Kubernetes-based GPU/TPU superclusters, optimize for AI/ML training, and mentor teams while innovating in ML infrastructure.