Senior Machine Learning Engineer – Road & Lane Detection New Montreal, Quebec, Canada

Torc Robotics, Inc.; Montreal; On-site; CAD 100,000 - 130,000; Full time

Job summary

An innovative autonomous vehicle company based in Montreal seeks a Senior Machine Learning Engineer to design and deploy deep learning models for road and lane detection. The role involves collaboration across teams and mentorship of junior engineers, requiring mastery in Python and PyTorch, and experience in perception systems. This is a critical position in shaping autonomous vehicle technology.

Qualifications

• 6+ years of experience or 4+ years with a master's degree.
• Experience with lane prediction and free space segmentation.
• Hands-on with perception systems in autonomous vehicles.

Responsibilities

• Design and deploy deep learning models for road detection.
• Collaborate with teams on performance validation.
• Mentor junior engineers and lead the ML modeling group.

Skills

Deep learning model design

Multi-modal sensor data optimization

Python programming

PyTorch mastery

Mentorship and leadership

Education

Bachelor’s degree in computer science or related field

Master’s degree in computer science or related field

PhD in machine learning or data science

Tools

CUDA

NVIDIA libraries (CUBLAS, CuDNN)

Ray

Job description

Overview

Senior Machine Learning Engineer – Road & Lane Detection

Meet the Team

At Torc Robotics, we are on a mission to revolutionize freight movement through safe, efficient, and reliable autonomous driving technology. Backed by Daimler Truck, we are industry leaders in Level 4 autonomous vehicle systems, with decades of innovation and a clear path to commercialization. Join our growing Model Development team and contribute to world-class machine learning systems for Road & Lane detection – a critical function in enabling autonomous vehicle perception and path planning.

This is a hands-on applied research and development role with direct impact on Torc’s core autonomy stack.

What You’ll Do

• Design, train, and deploy deep learning models for road and lane topology prediction, including drivable space, lane boundaries, and intersection structures.
• Build and optimize neural network architectures that leverage multi-modal sensor data (camera, LiDAR, radar) and SD / HD map context.
• Collaborate with teams across perception, mapping, planning, and systems integration to ensure seamless performance in real-world autonomous driving.
• Lead model ablation studies, error analysis, and performance validation using large-scale simulation and real-world datasets.
• Develop tooling and workflows to automate training, experimentation, and evaluation of ML models.
• Mentor junior engineers and contribute to technical leadership within the ML modeling group.

What You’ll Need to Succeed

• Bachelor’s degree in computer science, data science, artificial intelligence or related field with 6+ years of professional experience or a master’s degree with 4+ years of experience
• Hands-on experience with segmentation tasks like lane prediction, free space segmentation, etc.
• State-of-the-Art AV experience with multi-sensor data, especially in perception systems for autonomous vehicles or robotics.
• Mastery of Python and PyTorch, with the ability to transition research-level code to production and deployment-ready standards
• Proficiency in Python, and familiarity with modern ML Ops tools and GPU-based training.
• Prior experience in autonomous driving, robotics, or similar safety-critical domains.
• Experience with LiDAR, radar, or 3D spatial data processing.
• Knowledge of performance metrics for perception and prediction tasks (IoU, FDE, ADE, mAP).

Bonus Points

• PhD in machine learning or data science
• Proficient in writing CUDA kernels and developing custom PyTorch operations.
• Experience with relevant NVIDIA libraries and frameworks, such as CUBLAS, CuDNN, and NPP
• Proficiency with Ray
• Publications or contributions to open-source ML projects.
• C++ skills or experience integrating ML into production autonomy systems.