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
