AI Vision Developer

Waste Robotics

. Train, evaluate, compare, and deliver detection and segmentation models (YOLO, DETR, Mask R-CNN) .

Posted 9/15/2026full-timeMontréal • CanadaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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

Demonstrates expertise in deploying and optimizing detection and segmentation models using Python, with a strong focus on MLOps practices and collaboration across multidisciplinary teams. Proficient in evaluating model performance metrics and ensuring robust deployment in challenging environments.

Highest-signal resume keywords

Python Systems DesignObject DetectionMLOps PracticesModel Evaluation MetricsIndustrial Imaging

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

YOLODETRMask R-CNNPrecisionRecallF1 ScoreAP/mAPLatency OptimizationActive LearningAutomated Testing

Soft Skills

Excellent CommunicationCollaboration SkillsMentoringAnalytical MindsetSelf-Directed Work

Tools & Technologies

PyTorchONNXTensorRTRGB-D CamerasCUDACloud-Based Data PipelinesModel-Tracking Platforms

Industry Keywords

Detection ModelsSegmentation ModelsDomain ShiftImbalanced DataChallenging Environments

Tech Stack

Tools & technologies

PythonPyTorch

About the role

Key responsibilities & impact

Train, evaluate, compare, and deliver detection and segmentation models (YOLO, DETR, Mask R-CNN)
Diagnose errors and report comprehensive metrics (precision, recall, F1, AP/mAP, latency)
Deploy real-time inference at the edge
Conduct parity testing across environments
Optimize latency and throughput
Ensure traceable and reversible releases
Develop evaluation protocols robust to variations in conveyors, lighting, and cameras
Determine the minimum dataset required for a new site
Prioritize the most informative images for active annotation
Monitor the quality of model pre-annotations
Specify and validate RGB-D cameras, optics, and commissioning conditions
Advance picking performance at flagship facilities to 95%, with transparent class-level reporting
Collaborate with multidisciplinary teams in AI, software, hardware, operations, and domain expertise

Requirements

What you’ll need

Senior ownership of production Python systems, including design, testing, deployment, and support
Hands-on experience with object detection or instance segmentation, applying evaluation rigor beyond a single metric
Proven MLOps practices and deployment with PyTorch, ONNX, TensorRT, or equivalent technologies
Experience with domain shift, imbalanced data, or active learning
Strong software engineering skills, including modular design, automated testing, and documentation
Nice to have: industrial imaging (RGB-D, calibration, challenging environments), CUDA/GPU profiling, cloud-based data pipelines, and model-tracking platforms
Excellent communication and collaboration skills within a multidisciplinary team spanning AI, software, hardware, operations, and domain experts
Ability to mentor peers and communicate technical trade-offs directly, particularly during code and design reviews
Self-directed in organizing your work, with sound judgment in managing priorities
Analytical mindset, with the ability to challenge incomparable results and clearly explain experimental limitations
Professional communication skills in French and English
Currently residing in the Montreal or Trois-Rivières area; remote work from outside these regions and an intention to relocate in the future do not meet this requirement

Benefits

Comp & perks

4 weeks of vacation
A day off on your birthday
Comprehensive insurance coverage, including dental, an employee assistance program, and telemedicine
Team activities
End-to-end visibility into robotic picking performance, from the sensor to the production robot
A tangible impact through technology deployed across multiple countries in support of the circular economy