Senior Machine Learning Engineer – Camera Model

Torc Robotics

. Develop and deploy machine learning models for camera-based perception in autonomous trucks, including object detection, segmentation, depth estimation, and scene understanding .

Posted 9/21/2026full-timeRemote • CanadaSenior💰 CA$168,000 - CA$193,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in developing and deploying machine learning models for camera-based perception, with strong capabilities in Python and PyTorch. Proven ability to analyze model performance, improve training pipelines, and collaborate cross-functionally in autonomous systems.

Highest-signal resume keywords

Machine Learning Model DevelopmentDeep Learning for Computer VisionPython ProgrammingPyTorch FrameworkModel Performance Analysis

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Machine LearningDeep LearningObject DetectionSegmentationDepth EstimationScene UnderstandingData CurationModel EvaluationDistributed TrainingExperiment Tracking

Soft Skills

CollaborationMentoringProblem-SolvingCommunication

Tools & Technologies

Large-Scale DatasetsDistributed Compute EnvironmentsExperiment Management FrameworksRayCamera Calibration

Industry Keywords

Autonomous DrivingRoboticsPerception Systems3D PerceptionGeometric Reasoning

Tech Stack

Tools & technologies

PythonPyTorchRay

About the role

Key responsibilities & impact

Develop and deploy machine learning models for camera-based perception in autonomous trucks, including object detection, segmentation, depth estimation, and scene understanding
Own end-to-end model development from data curation and training through evaluation and deployment
Write production-quality ML code for scalable training, evaluation, and inference pipelines
Analyze model performance across diverse driving scenarios, identify failure modes, and improve robustness and generalization
Improve large-scale training pipelines, including dataset preparation, distributed training, and experiment tracking
Partner with data teams on dataset quality, labeling strategies, and edge-case coverage
Collaborate with perception, simulation, and validation teams to integrate models into the autonomy stack
Improve tooling, workflows, and infrastructure for experimentation and model iteration
Contribute to model architecture decisions and technical discussions
Mentor junior engineers on implementation, debugging, and best practices

Requirements

What you’ll need

Bachelor’s degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related technical field with 6+ years of industry experience, OR Master’s degree with 3+ years OR PhD with 1+ years of experience
Experience developing and deploying deep learning models for computer vision or perception systems
Strong programming skills in Python and PyTorch, with experience writing production-quality ML code
Experience training and evaluating models using large-scale datasets and distributed compute environments
Solid understanding of modern deep learning architectures used in perception, including CNNs, transformers, and multi-task models
Experience debugging model behavior, analyzing performance metrics, and improving model reliability
Ability to translate ambiguous problems into structured ML solutions and deliver independently
Experience collaborating cross-functionally to integrate ML models into larger autonomy or robotics systems
Bonus: Experience in autonomous driving, robotics, or simulation-based ML systems
Bonus: Experience with multi-task learning or unified perception architectures
Bonus: Experience with large-scale data pipelines, distributed training systems such as Ray, or experiment management frameworks
Bonus: Familiarity with camera calibration, geometric reasoning, or 3D perception from images, including BEV, monocular depth, and structure-from-motion
Bonus: Experience deploying ML models into production or real-world robotics systems

Benefits

Comp & perks

A competitive compensation package that includes a bonus component and stock options
Medical, dental, and vision for full-time employees
RRSP plan with a 6% employer match
Public Transit Subsidy (Montreal area only)
Flexibility in schedule and generous paid vacation
Company-wide holiday office closures
Life Insurance
Potential sign-on payments, relocation, and other forms of compensation may be provided as part of a total compensation package