Torc Robotics - Senior, Machine Learning Engineer - Camera Model
Requirements
• Bachelor’s degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related technical field with 6+ years of industry experience, OR Master’s degree with 3+ years OR PhD with 1+ years of experience • Experience developing and deploying deep learning models for computer vision or perception systems • Strong programming skills in Python and PyTorch, with experience writing production-quality ML code • Experience training and evaluating models using large-scale datasets and distributed compute environments • Solid understanding of modern deep learning architectures used in perception (e.g., CNNs, transformers, multi-task models) • Experience debugging model behavior, analyzing performance metrics, and improving model reliability • Ability to translate ambiguous problems into structured ML solutions and deliver independently • Experience collaborating cross-functionally to integrate ML models into larger autonomy or robotics systems • 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 (e.g., Ray), or experiment management frameworks • Familiarity with camera calibration, geometric reasoning, or 3D perception from images (e.g., BEV, monocular depth, structure-from-motion) • Experience deploying ML models into production or real-world robotics systems • Work Location: For this position, we are open to hiring in Montreal, Canada or remote within Canada • Work Location: • Perks of Being a Full-time Torc’r (Canada)Torc cares about our team members and we strive to provide benefits and resources to support their health, work/life balance, and future. Our culture is collaborative, energetic, and team focused. Torc offers: • Perks of Being a Full-time Torc’r (Canada) • A 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 (Montreal area only) • Flexibility in schedule and generous paid vacation • Company-wide holiday office closures • At Torc, we’re committed to building a diverse and inclusive workplace. We celebrate the uniqueness of our Torc’rs and do not discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, veteran status, or disabilities. Even if you don’t meet 100% of the qualifications listed for this opportunity, we encourage you to apply. Our compensation reflects the cost of labor across several geographic markets. Pay is based on a number of factors and may vary depending on job-related knowledge, skills, and experience. Torc's total compensation package will also include our corporate bonus and stock option plan. Dependent on the position offered, sign-on payments, relocation, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. Job ID: 102952 • Job ID:
Responsibilities
• Design, develop, and deploy deep learning models for camera-based perception (e.g., object detection, segmentation, depth estimation, scene understanding) • Own end-to-end model development for scoped areas, from data curation and training to evaluation and deployment • Write production-quality ML code to support scalable training, evaluation, and inference pipelines • Analyze model performance across diverse driving scenarios, identify failure modes, and improve robustness and generalization • Contribute to and improve large-scale training pipelines, including dataset preparation, distributed training, and experiment tracking • Partner with data teams to improve dataset quality, including labeling strategies and coverage of edge cases • Collaborate with perception, simulation, and validation teams to evaluate and integrate models into the autonomy stack • Improve tooling, workflows, and infrastructure to accelerate experimentation and model iteration • Contribute to model architecture decisions and technical discussions within the team • Mentor junior engineers on implementation, debugging, and best practices
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