Senior, Machine Learning Engineer - Camera Model

Role overview A senior individual-contributor role owning camera-based perception machine learning for autonomous long-haul trucking. The position focuses on taking scoped perception problems from research through to production, working across model architecture, large-scale training pipelines, and integration into the broader autonomy stack. It is well suited to an ML engineer who wants deep ownership of vision models in safety-critical robotics.

Responsibilities - Design, develop, and deploy deep learning models for camera-based perception tasks such as object detection, segmentation, depth estimation, and scene understanding. - Drive end-to-end model development for scoped areas, covering data curation, training, evaluation, and deployment to the autonomy stack. - Write production-quality ML code that powers scalable training, evaluation, and inference pipelines, and contribute to large-scale dataset preparation and distributed training workflows. - Analyze model performance across diverse driving scenarios, identify failure modes, and iterate to improve robustness and generalization. - Collaborate with data, perception, simulation, and validation teams to refine labeling strategies, expand edge-case coverage, and integrate models into the autonomy stack. - Improve tooling and infrastructure for experimentation and model iteration, contribute to architecture decisions, and mentor junior engineers.

Requirements - Bachelor's degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related field with 6+ years of industry experience, or Master's with 3+ years, or PhD with 1+ year. - Track record of developing and deploying deep learning models for computer vision or perception systems. - Strong Python and PyTorch programming skills, with experience writing production-grade ML code. - Hands-on experience training and evaluating models on large-scale datasets in distributed compute environments. - Solid grasp of modern perception architectures including CNNs, transformers, and multi-task models, plus the ability to debug model behavior and interpret performance metrics. - Demonstrated ability to translate ambiguous problems into structured ML solutions and collaborate cross-functionally on autonomy or robotics systems.

Nice to have - Background in autonomous driving, robotics, or simulation-based ML, including multi-task learning or unified perception architectures. - Experience with large-scale data pipelines, distributed training frameworks such as Ray, or experiment management tooling. - Familiarity with camera calibration, geometric reasoning, or 3D perception from images (for example bird's-eye-view, monocular depth, or structure-from-motion). - Prior experience deploying ML models into real-world production or robotics systems.

Benefits and work setup - Open to candidates in Montreal or remote elsewhere in Canada, with a competitive compensation package including bonus and stock options. - Medical, dental, and vision coverage, RRSP plan with 6% employer match, life insurance, and a public transit subsidy for Montreal hires. - Flexible scheduling, generous paid vacation, and company-wide holiday office closures; salary range listed at CAD $168,000-$193,000.