Torc Robotics
Senior, Machine Learning Engineer - Camera Model
Remote - Canada, Montreal, Canada +1 · Remote · Senior · Full-time · Posted Sep 21, 2026
- Location
- Remote - Canada, Montreal, Canada
Remote - US - Experience
- Bachelor’s degree with 6+ years of industry experience, or Master’s degree with 3+ years, or PhD with 1+ years.
- Education
- Bachelor’s degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related technical field; alternatively, a Master’s degree or PhD.
About the job
Develops and deploys camera-based machine learning models for autonomous-truck perception, from data preparation and training through evaluation and production integration.
What you'll do
- Own end-to-end development of perception models and improve their robustness across driving scenarios.
- Build and improve scalable training, evaluation, and inference pipelines, including dataset preparation and distributed training.
- Collaborate across perception, data, simulation, and validation teams to integrate models into the autonomy stack; mentor junior engineers.
What they ask for
- Experience developing and deploying deep learning models for computer vision or perception systems.
- Strong programming skills in Python and PyTorch, including production-quality ML code.
- Experience training and evaluating models using large-scale datasets and distributed compute environments.
- Understanding of modern perception architectures, and experience debugging model behavior and improving reliability.
Nice to have
- Experience in autonomous driving, robotics, or simulation-based ML systems.
- Experience with distributed training systems such as Ray, camera calibration, or 3D perception from images.
Pay
$168,000-$193,000 CAD (salary range; base salary or total pay not specified)
Tools
Python, PyTorch, Ray, CNNs, transformers
AI focus
Develops deep learning models for camera-based perception, including object detection, segmentation, depth estimation, and scene understanding for autonomous trucks.
Model-written summary · the original posting is authoritative