Senior Machine Learning Engineer - Computer Vision

Citylogix • Montreal, QC

1d

About The Position

Reporting to the Chief Technology Officer (CTO), the Senior Machine Learning Engineer is responsible for researching, developing, and deploying the computer-vision models at the core of our platform — the segmentation systems that detect infrastructure defects and map road assets from large-scale imagery. This is a hands-on technical role focused on transformer-based segmentation. You will own models from research through production: improving accuracy and robustness, generalizing models across different camera systems, consolidating task-specific models into unified architectures, and partnering with our DevOps and Engineering teams to bring them reliably into production at scale.

Requirements

• Minimum of 5 years of applied machine-learning / computer-vision experience, or a graduate degree (MSc/PhD) ina relevant field plus 3+ years of applied experience
• Strong, hands-on proficiency with PyTorch
• Demonstrated experience with deep-learning segmentation (semantic, instance, and/or panoptic) on real-world image data
• Practical experience with transformer-based vision architectures (Mask2Former, OneFormer, DETR, or similar)
• Solid command of fine-tuning, transfer learning, and domain-adaptation/generalization techniques
• Experience with the Hugging Face ecosystem and/or detectron2
• Comfort training and deploying models in a cloud environment (AWS preferred; experience with Amazon SageMaker a strong asset)
• Strong Python engineering skills and good software practices (version control, testing, reproducibility)
• Excellent communication and collaboration skills, and the ability to manage priorities in a fast-paced environment

Nice To Haves

• Experience working with imagery from industrial, line-scan, or panoramic camera systems
• Background in geospatial / GIS, remote sensing, or infrastructure/civil applications
• MLOps experience: model lifecycle management, monitoring, and CI/CD for ML
• Model-optimization experience (quantization, distillation, ONNX/TensorRT, or similar) for efficient inference
• Experience unifying multi-task or multi-model systems into consolidated architectures
• Peer-reviewed publications or open-source contributions in computer vision
• Experience in a fast-growing SaaS or technology-driven organization

Responsibilities

• Research, design, train, and evaluate deep-learning models for semantic, instance, and panoptic segmentation, applied to infrastructure-defect detection and road-asset/scene segmentation
• Fine-tune and adapt transformer-based segmentation architectures (e.g., Mask2Former, OneFormer, and DETR-family models, including custom in-house variants)
• Improve domain generalization so models perform reliably across heterogeneous camera systems and imaging conditions, both individually and in combination
• Consolidate multiple task- or defect-specific checkpoints into unified, multi-task models to simplify the production footprint
• Partner with the DevOps team to train, deploy, and monitor models on AWS (including Amazon SageMaker), and to optimize inference performance and cost
• Build and maintain reproducible training pipelines, evaluation benchmarks, and dataset/annotation workflows
• Diagnose model failures and data-quality issues, and drive measurable improvements in accuracy, robustness, and efficiency
• Track emerging architectures and techniques, and recommend where they create real value for the platform
• Maintain clear technical documentation, experiment records, and model-versioning standards

What This Job Offers

• Job Type: Full-time
• Career Level: Senior