Ingénieur(e) “Staff" en apprentissage automatique — Détection de route et de voie
Torc Robotics14 days ago
Responsibilities
- Own the model roadmap for road and lane detection from concept to production maturity.
- Research, design, and train advanced neural architectures for road structure detection.
- Lead data strategy, defining curation, labeling policies, and active learning pipelines.
- Develop robust metrics and evaluation frameworks for road geometry accuracy.
- Advance core capabilities like self-supervised pre-training and temporal modeling.
- Conduct large-scale experiments to identify scalable improvements.
- Collaborate with perception teams to ensure model and interface consistency.
- Mentor engineers and define best practices for model training and evaluation.
Requirements
- 10+ years of experience in deep learning model development for perception or computer vision.
- Master's or PhD in computer science, electrical engineering, robotics, or related field.
- Deep expertise in semantic and instance segmentation, BEV modeling, or scene topology estimation.
- Strong understanding of road geometry modeling and sensor calibration.
- Proficiency in Python and modern machine learning frameworks like PyTorch.
- Experience with distributed training pipelines and large-scale dataset management.
- Proven leadership in guiding technical roadmaps and mentoring engineers.
Benefits
- Competitive compensation package including bonuses and stock options.
- Comprehensive medical, dental, and vision coverage for full-time employees.
- Retirement savings plan with a 4% employer contribution.
- Public transit subsidy (only in the Montreal area).
- Flexible hours and generous paid vacation.
- Company-wide office closures during holidays.
- Life insurance.