Top Tech Jobs & Startup Jobs in Montreal

Lead North's integrations strategy and execution: prioritize, ship, and drive adoption of connectors and ecosystem tooling. Partner with customers and engineering to convert integrations into reusable platform capabilities (auth, connector framework, data sync, permissions). Define developer/partner experience (APIs, SDKs, plugins) and instrument analytics to measure adoption, time-to-stand-up, and retention.

The Staff Software Engineer will develop and deploy high-performance machine learning systems, optimizing large language models for production environments. Responsibilities include ensuring high availability and low latency for AI applications and working collaboratively across teams.

The Site Reliability Engineer will develop, deploy, and operate AI infrastructure, focusing on high-performance and scalable machine learning systems using Kubernetes and cloud platforms.

As a Forward Deployed Engineer, you will build and ship features for an AI workspace platform, develop autonomous agents, and manage end-to-end deployments, collaborating closely with clients in various sectors.

As a Data Annotation Specialist, you will label, audit, and optimize data for AI models in Chinese while ensuring high accuracy and quality. You will perform repetitive tasks and collaborate with teams, providing feedback and recommendations for data improvement.

Build and ship full-stack features for North, develop autonomous agents that access sensitive enterprise data, productionize state-of-the-art models, write minimal, secure low-resource code, and collaborate with researchers to deploy performant RAG/agentic applications while prioritizing security and user experience.

As a Software Engineer in Data Infrastructure, you will build and maintain high-performance data storage systems for AI training, collaborate with experts, and optimize data processing.

As a Data Annotation Specialist, you'll label, audit, and correct machine learning data while ensuring high accuracy in Japanese. You'll also evaluate responses and recommend optimizations based on project guidelines.

Provide legal guidance for product development and commercialization: create templates and playbooks, manage IP and open source compliance, mitigate product lifecycle risks, coordinate legal input for launches, advise product, engineering and business teams, and monitor relevant technology regulations.

Lead and mentor a GPU infrastructure engineering team to design, deploy, and scale GPU/TPU clusters. Define technical roadmap, implement topology-aware scheduling, monitoring, IaC, and cost/capacity optimization. Collaborate with AI researchers, cloud providers, and cross-functional teams to ensure reliable, secure, and performant infrastructure.