Works on images and video — detection, recognition, inspection, medical imaging.
A computer vision engineer builds systems that understand images and video — object detection, image classification, facial recognition, medical imaging analysis, autonomous vehicle perception, quality inspection in manufacturing, and more. It is one of the more specialised AI roles and often requires stronger mathematics than other AI engineering positions.
Computer vision has real-world deployment challenges that text-based AI does not face: running models on edge devices with limited compute, handling variable lighting and camera conditions, processing video streams in real-time, and dealing with safety-critical applications where model errors have physical consequences.
Implement detection and classification pipelines using pre-trained models. Learn the standard architectures (ResNet, YOLO, SAM). Build evaluation scripts.
Design vision systems for specific domains. Fine-tune and optimise models for production constraints. Own the vision pipeline for a product.
Architect end-to-end vision systems. Make hardware-software co-design decisions. Lead deployment on edge devices and production infrastructure.
Define the vision technology strategy. Evaluate emerging architectures. Influence product roadmaps based on what computer vision can deliver.
Already working in another field? Here is how your background maps.
Learn PyTorch, computer vision fundamentals (convolutions, architectures) and linear algebra. Build an object detection project with YOLO. Your production engineering skills are valuable — many CV engineers struggle with deployment.
A strong match for edge AI roles. Learn PyTorch and vision architectures, then model optimisation for constrained hardware. Your embedded systems knowledge is a rare and valuable combination.
Medical imaging is a growing field. Learn Python, PyTorch and basic CV concepts. Your domain knowledge of anatomy and pathology combined with CV skills makes you very employable in health-tech.
