Senior Machine Learning Engineer – Road & Lane Detection

Torc Robotics; Montreal; On-site; CAD 80,000 - 100,000; Full time

Job summary

A leading autonomous driving technology firm in Montreal is seeking a Senior Machine Learning Engineer. The role involves designing and deploying deep learning models for road feature prediction, collaborating with various teams to ensure seamless autonomous driving performance. Ideal candidates should have extensive experience in machine learning and multi-sensor data analysis, particularly in the context of autonomous vehicles. This position offers significant impact on core autonomy systems.

Qualifications

• 6+ years of professional experience or a master's degree with 4+ years of experience.
• Hands-on experience with segmentation tasks like lane prediction.
• State of the Art AV experience with multi-sensor data.

Responsibilities

• Design, train, and deploy deep learning models for road prediction.
• Optimize neural network architectures leveraging sensor data.
• Collaborate with teams to ensure performance in autonomous driving.

Skills

Deep learning

Python

PyTorch

Multi-sensor data analysis

Neural network architecture

Education

Bachelor's degree in relevant field

Master's degree

Tools

CUDA

LiDAR

NVIDIA libraries

Job description

Overview

At Torc Robotics, we\'re 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 directly contribute to our world-class machine learning systems for Road & Lane detection– a critical function in enabling AV perception and path planning. We are seeking a highly motivated Senior Machine Learning Engineer to join our Road & Lane Detection team focused on developing robust models that predict static and semi-static road features (lanes, intersections, boundaries, driveable space, etc). You will be responsible for designing and implementing state-of-the-art deep learning models that enable our autonomous vehicles to understand and anticipate road structures in diverse environments. 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.