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.

• 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.

• 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.

• 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.

• 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.