About the role

Staff Engineer in machine learning at Torc Robotics developing next-gen perception models. Leading architectural innovations and large-scale model training for autonomous vehicle technology.

Responsibilities

Lead the development of the BEV model: define and execute the technical roadmap for BEV-based perception models across multiple tasks (e.g., detection, segmentation, road topology, and scene understanding).
Design advanced multimodal architectures that fuse heterogeneous sensor data (camera, LiDAR, radar, HD maps) into unified spatial representations.
Develop foundational perception models leveraging BEV transformers, voxel-based encoders, or implicit scene representations.
Own large-scale training workflows, from data sampling strategies and augmentation pipelines to distributed training and hyperparameter optimization.
Improve model robustness and generalization, accounting for long-tail conditions such as low visibility, occlusions, and rare scene configurations.
Establish evaluation frameworks for geometric accuracy, temporal stability, and cross-domain transfer performance.
Collaborate cross-functionally with sensor calibration, mapping, and fusion teams to ensure cohesive perception model interfaces.
Mentor and guide machine learning engineers, cultivating best practices in experimentation, code quality, and model validation.
Remain at the forefront of machine learning research, exploring self-supervised learning, large-scale pretraining, and foundation models for 3D perception.

Master’s degree or PhD in Computer Science, Electrical Engineering, Robotics, or a related field (or equivalent practical experience).
10+ years of experience in deep learning for perception, 3D vision, or autonomous systems.
Proven expertise in BEV modeling, 3D scene understanding, and multi-view fusion.
Strong experience in multimodal sensor fusion, particularly integrating camera and LiDAR data.
Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
Experience with large-scale data pipelines, distributed training, and experiment/experiment-management systems.
Demonstrated leadership in driving ML model innovation and mentoring technical teams.

Competitive compensation program including bonus components and stock option grants
Medical, dental, and vision coverage for full-time employees
Retirement savings plan (RRSP) with a 4% employer contribution
Public transit subsidy (Montreal region only)
Flexible work hours and generous paid time off
Company-wide office closures during major holidays
Life insurance