Top Tech Jobs & Startup Jobs in Montreal

Evaluate and label model-generated and human-written software engineering outputs for coding, debugging, and codebase-related tasks. Assess agent trajectories, prompt models for complex coding tasks, proofread and improve responses, and report quality and performance trends. Part-time contractor role contributing to model development.

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 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.

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.

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.

Build and maintain production-grade data processing systems and foundational infrastructure for enterprise AI. Collaborate with researchers and engineers, deliver end-to-end implementations, transform unstructured data into performant datasets, and use distributed processing frameworks and modern analytics tooling to inform products and strategy.