Senior, Machine Learning Engineer - Camera Model
About this role
To address the need for advanced vision systems in automated freight trucks, Torc Robotics requires a senior professional to build and maintain camera-based perception models. The Camera Models team develops the core technology that allows the autonomy stack to understand environments, detect objects, and interpret scene structure from camera inputs.
In this capacity, the engineer will own the end-to-end lifecycle of deep learning models, ranging from data curation and training to evaluation and production deployment. This position suits individuals who can transform ambiguous challenges into structured machine learning solutions, analyze failure modes, and collaborate closely with cross-functional teams across perception, data, and simulation.
What you will do
- Design, develop, and deploy deep learning models for camera-based perception tasks such as object detection, segmentation, depth estimation, and scene understanding.
- Own end-to-end model development for scoped areas by handling data curation, training, evaluation, and deployment.
- Write production-quality machine learning code to support scalable training, evaluation, and inference pipelines.
- Analyze model performance across diverse driving scenarios, identify failure modes, and improve model robustness and generalization.
- Contribute to and enhance large-scale training pipelines that include dataset preparation, distributed training, and experiment tracking.
- Partner with data teams to refine dataset quality, labeling strategies, and edge case coverage.
- Collaborate with perception, simulation, and validation teams to integrate models into the autonomy stack while mentoring junior engineers on best practices.
What you need
- Hold a Bachelor’s degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related field with at least 6 years of industry experience, a Master’s degree with 3+ years, or a PhD with 1+ years.
- Demonstrate hands-on experience in developing and deploying deep learning models for computer vision or perception systems.
- Possess strong programming skills in Python and PyTorch alongside the ability to write production-quality machine learning code.
- Bring experience in training and evaluating models using large-scale datasets and distributed compute environments.
- Show a solid understanding of modern deep learning architectures utilized in perception, including CNNs, transformers, and multi-task models.
- Exhibit the capability to debug model behavior, analyze performance metrics, and improve overall model reliability.
- Demonstrate the ability to translate ambiguous problems into structured machine learning solutions and deliver independently.
Nice to have
- Experience in autonomous driving, robotics, or simulation-based ML systems
- Experience with multi-task learning or unified perception architectures
- Experience with large-scale data pipelines, distributed training systems like Ray, or experiment management frameworks
- Familiarity with camera calibration, geometric reasoning, or 3D perception from images such as BEV, monocular depth, and structure-from-motion
- Experience deploying ML models into production or real-world robotics systems
Benefits
- Competitive compensation package that includes a bonus component and stock options
- Medical, dental, and vision for full-time employees
- RRSP plan with a 6% employer match
- Public Transit Subsidy for the Montreal area only
- Flexibility in schedule and generous paid vacation
- Company-wide holiday office closures and Life Insurance
Skills and tech stack
Questions candidates ask
What is the remote work policy for this position?
For this position, Torc Robotics is open to hiring in Montreal, Canada or remote within Canada.
What is the salary range for this role?
The salary range for this position is $168,000-$193,000 CAD. Pay is based on a number of factors and may vary depending on job-related knowledge, skills, and experience.
What technologies and frameworks will I be working with?
The tech stack includes Python and PyTorch, with deep learning architectures like CNNs and transformers. You will also work with large-scale training pipelines and distributed compute environments.