Top Tech Jobs & Startup Jobs in Montreal

Design, deploy, and operate large-scale workflow orchestration platforms (Argo/Airflow/hybrid) for simulation, ML training, data pipelines and CI/CD. Build SDKs, abstractions, and self-service tooling; ensure reliability, observability, cost-efficiency, GPU and cross-cluster scheduling on Kubernetes across cloud and on-prem; integrate with storage, streaming, registries, and CI/CD; document best practices and mentor engineers.

Design, implement, and validate motion planning, behavior, prediction, and online mapping algorithms for autonomous trucks. Build learning-based prediction/planning pipelines, integrate models on-vehicle, root-cause road and simulation failures, and collaborate across perception, control, simulation, and operations to ship validated releases.

Develop, train, and optimize deep learning models for autonomous driving (perception, mapping, end-to-end planning). Execute full ML lifecycle from data curation to deployment, collaborate with simulation and infrastructure teams, and evaluate SOTA research to address real-world corner cases.

Develop and research deep learning and vision algorithms for autonomous driving (detection, tracking, prediction, planning, SLAM). Lead end-to-end ML projects from data analysis through model evaluation, and collaborate across product, simulation, and algorithm teams to extend ML technologies into system components.

Build high-performance, scalable simulation infrastructure and tooling for autonomous vehicle development. Design systems for scenario generation, simulation execution, synthetic data, and reinforcement-learning workflows. Collaborate with ML, perception, and controls teams to improve realism, performance, and validation pipelines while writing production-quality C++ and Python code, contributing to architecture, testing, and documentation.

Build, test, and maintain software across full-stack, backend, infrastructure, data pipelines, and ML infrastructure to support autonomous trucking. Develop web interfaces, backend services, APIs, and tooling; improve reliability, observability, CI/CD, and deployment; participate in code reviews, debugging, and cross-team collaboration while learning new technologies.

Develop and train conditioned policies and MARL systems to simulate realistic driving behaviors, implement safety-constrained RL algorithms, design rewards and evaluation metrics, optimize large-scale training pipelines, advance neural architectures for long-horizon planning and spatial reasoning, and integrate research models with production simulation and planning teams.

The Senior ML/RL Engineer will develop behavior models, implement RL algorithms focusing on safety, design reward functions and optimize training environments while collaborating across teams.

Optimize end-to-end GPU performance for real-time autonomous driving workloads: develop and optimize parallel GPU algorithms and inference pipelines, profile and eliminate bottlenecks across computation and memory, debug GPU software for latency/throughput improvements, and collaborate on onboard GPU software architectures for embedded platforms.

Manage full-cycle technical recruiting for a Bay Area startup: source, screen, coordinate interviews, maintain ATS, partner with hiring managers, build pipelines for software, ML, robotics and autonomous driving roles, support events and university outreach, and improve recruiting processes.