Location: Toronto, Ontario

Location is Remote, but Toronto is preferred. Montreal is a secind option and lastly anywhere in a larger metropolitan centre

Our client is a venture-backed applied AI company building advanced software platforms that automate complex visual and operational workflows for technical industries. Their products combine computer vision, machine learning, multimodal AI, and workflow automation to solve highly manual, data-intensive problems at scale.

The company is entering a significant growth phase and is investing heavily in engineering, AI infrastructure, and product innovation. The environment is highly collaborative, fast-moving, and deeply technical, with a strong focus on practical AI applications deployed into real production environments.

About the Role

This is a hands-on Machine Learning / Computer Vision Engineering role focused on building and deploying production-grade vision systems used in complex real-world workflows.

The team already has strong machine learning and backend engineering capabilities in place. This hire is intended to add deeper expertise in computer vision, image understanding, and multimodal AI systems.

The role will involve a blend of:

traditional computer vision techniques, modern deep learning approaches, multimodal / vision-language model integration, and production ML engineering experimentation and rapid prototyping

The ideal candidate enjoys operating in a startup environment where speed, ownership, and practical problem solving matter as much as technical depth.

What You’ll Be Doing

Design, train, and optimize advanced computer vision models for detection, segmentation, classification, and visual reasoning tasks

Build scalable training, inference, and experimentation pipelines using Python and modern ML tooling

Work with both classical computer vision approaches and modern multimodal / vision-language systems

Develop and improve data processing, annotation, augmentation, and evaluation workflows

Deploy and maintain production ML systems in cloud-based environments

Improve model reliability, performance, observability, and scalability across production workloads

Partner closely with product and engineering teams to integrate AI capabilities into customer-facing platforms and APIs

Contribute to architectural discussions, technical strategy, and ongoing platform evolution

Must Have Skills:

Strong hands-on experience building and deploying computer vision systems in production environments

Deep understanding of computer vision fundamentals, image processing, and deep learning architectures

Strong experience with Python, PyTorch, OpenCV, and modern ML tooling

CNNs, segmentation, and detection architecture

Exposure to OCR, geometric reasoning, or 3D transformation workflows

Experience working with multimodal AI systems, vision-language models, or applied generative AI workflows

Experience building scalable ML pipelines and inference systems

Strong understanding of model evaluation, optimization, and experimentation methodologies

Comfortable operating in fast-paced, highly iterative startup environments

Strong communication and collaboration skills across engineering and product teams

Nice to Have Skills:

Experience deploying ML systems using Docker, AWS, or modern cloud infrastructure

Background in applied AI products serving technical or operational industries

Master’s or PhD in Computer Vision, Machine Learning, Computer Science, or related fields

Experience working in smaller high-growth startups or highly autonomous engineering teams