Machine Learning Engineer II

Torc Robotics

Ingénieur·e en apprentissage automatique, II chez Torc Robotics développant et déployant des modèles de comportements pour camions autonomes. Travaillant au sein d'une équipe pluridisciplinaire pour améliorer la prise de décision dans les transports.

Posted 7/2/2026full-timeMontreal • Missouri • 🇺🇸 United StatesMid-LevelSenior💰 CA$116,500 - CA$174,800 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and training machine learning models for autonomous systems, with a strong focus on behavioral cloning, imitation learning, and reinforcement learning. Proficient in implementing production-quality ML code and collaborating with multidisciplinary teams to enhance model performance and integration.

Highest-signal resume keywords

Machine Learning Model DevelopmentPython ProgrammingPyTorch FrameworkImitation LearningReinforcement Learning

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning TechniquesModel EvaluationData Workflow ManagementPerformance AnalysisModel Architecture Understanding

Soft Skills

CollaborationTechnical Discussion

Tools & Technologies

Simulation ToolsScalable Computing Environments

Industry Keywords

Autonomous SystemsRoboticsBehavioral CloningSequence ModelingModel Training Pipelines

Tech Stack

Tools & technologies

FluxPythonPyTorch

About the role

Key responsibilities & impact

Develop and train machine learning models for learned behavior systems, including approaches such as behavioral cloning, imitation learning, and reinforcement learning.
Implement production-quality ML code to support model training, evaluation, and inference within the autonomy stack.
Analyze model performance, identify failure modes, and propose improvements to increase robustness and generalization across different scenarios.
Contribute to model training pipelines and data workflows, organizing behavior datasets from simulation, fleet logs, and vehicle data.
Collaborate with simulation, validation, and autonomy engineering teams to test and evaluate learned behavior models across diverse driving environments.
Help integrate learned behavior models into simulation and test workflows to enable faster iteration and more comprehensive validation.
Support the development of tools and infrastructure that improve experimentation speed, repeatability, and model iteration.
Contribute to technical discussions regarding model architectures and training strategies within the team.

Requirements

What you’ll need

Bachelor's degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related technical field with at least 4 years of relevant experience, or a Master's degree with at least 2 years of experience.
Experience applying machine learning techniques—such as imitation learning, reinforcement learning, or sequence modeling—to robotics, autonomous systems, or complex control environments.
Strong programming skills in Python and PyTorch, with experience writing production-quality ML code.
Experience training and evaluating machine learning models using large datasets and scalable computing environments.
Understanding of ML architectures used in autonomous driving systems, such as transformers, graph neural networks, or sequence models.
Experience debugging model behavior, analyzing performance metrics, and iterating on training pipelines.
Ability to collaborate with multidisciplinary teams to integrate ML models into larger software systems.

Benefits

Comp & perks

Competitive compensation package, including bonuses and stock option grants
Medical, dental, and vision coverage for full-time employees
Registered Retirement Savings Plan (RRSP) with a 6% employer contribution
Public transit subsidy (Montreal region only)
Flexible working hours and generous paid time off
Company-wide office closures for public holidays
Life insurance