Top Tech Jobs & Startup Jobs in Montreal

Develop and maintain components for machine learning inference platform. Focus on Kubernetes deployments, resource management, and performance monitoring.

Build and maintain responsive, accessible frontend interfaces using React and TypeScript. Collaborate with product, design, and backend teams to launch performant, scalable UIs, create reusable component libraries, define APIs, mentor engineers, and drive frontend best practices.

Build an internal AI developer platform that provisions agent configs, context tooling, eval harnesses, and rollout mechanics to make AI-assisted engineering the default. Integrate LLMs and third-party coding tools, enable self-serve agent creation, embed safety and audit controls, and measure impact on developer productivity.

The GPU Kernel Engineer will design and implement high-performance GPU kernels, optimize code, and collaborate with research teams to enhance AI workloads.

Lead development of enterprise platform features focusing on infrastructure, security, and compliance for enterprise customers. Oversee projects, collaborate cross-functionally, and mentor engineering teams.

As an AI Support Engineer, you'll manage support requests, resolve user issues, optimize ML models, and contribute to product development.

As a Site Reliability Engineer, you'll build and maintain infrastructure for ML models, automate processes, and collaborate cross-functionally.

Build and evolve scalable observability systems: high-throughput telemetry ingest, cost-efficient storage, instrumentation libraries, SLO/alerting infrastructure, and integrations to improve reliability, diagnostics, and incident response across multi-cloud infrastructure.

The Software Engineer will lead the development of Baseten Voice AI, focusing on real-time model serving systems, collaboratng with teams on architecture and implementation.

Partner with customers to architect, build, and deploy high-scale production AI applications. Translate ambiguous goals into reliable, observable services, implement end-to-end model deployment and monitoring, optimize ML projects, and own customer-facing technical delivery and product outcomes.