Staff ML Engineer - Learned Behaviors & RL for Trucks

Torc Robotics; Mount Royal; Hybrid; CAD 209,000 - 314,000; Full time

Job summary

A leading autonomous vehicle technology company in Montreal seeks a Staff ML Engineer to lead the development of reinforcement learning-driven behavior models for autonomous trucks. This role requires over 10 years of experience in applied machine learning and strong programming skills in Python and Pytorch. The company offers a competitive compensation package, including stock options, medical benefits, and flexibility in work schedule. A diverse and inclusive workplace is promoted, encouraging applicants from various backgrounds.

Benefits

Competitive compensation package

Medical, dental, and vision insurance

RRSP plan with employer match

Public Transit Subsidy

Flexible schedule

Generous paid vacation

Qualifications

• 10+ years of experience in applied machine learning in autonomous vehicles.
• Proven track record of designing learned behavior models in production systems.
• Strong programming skills in Python.

Responsibilities

• Lead architecture, development, and validation of behavior models for autonomy.
• Define and implement data strategies for large behavior datasets.
• Develop scalable model training pipelines for quick iterations.

Skills

Applied machine learning

Python

Pytorch

Distributed computing

Model training

Reinforcement learning

Education

M.S. or Ph.D. in Computer Science, Robotics, or related field

Tools

Pytorch (Lightning)

Ray

Job description

A leading autonomous vehicle technology company in Montreal seeks a Staff ML Engineer to lead the development of reinforcement learning-driven behavior models for autonomous trucks. This role requires over 10 years of experience in applied machine learning and strong programming skills in Python and Pytorch. The company offers a competitive compensation package, including stock options, medical benefits, and flexibility in work schedule. A diverse and inclusive workplace is promoted, encouraging applicants from various backgrounds.