Computer vision engineer job description.
Hiring a computer vision engineer or joining the world of AI image processing? This job description explains tasks such as training image recognition models, working with neural networks, and deploying CV algorithms. It includes the skills in demand, career growth areas, and competitive salaries in the UK.
What does a computer vision engineer do?
A computer vision engineer develops algorithms and models that allow machines to interpret and process visual data from images or videos. Their work is used in facial recognition, object detection, medical imaging, robotics, and more.
Key responsibilities include training deep learning models, preprocessing image data, optimising performance for real-time applications, and integrating solutions into products. They often work with tools like OpenCV, PyTorch, TensorFlow, and cloud platforms.
In startups, they may build full pipelines from model design to deployment. In larger companies, they specialise in components such as annotation workflows, edge inference, or model accuracy improvements across specific use cases.
Key responsibilities of a computer vision engineer.
Computer vision engineers build systems that extract meaning from images and videos. Their responsibilities include:
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Developing and training models for object detection, tracking, and classification
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Preprocessing visual data and curating image/video datasets
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Using frameworks such as OpenCV, TensorFlow, or PyTorch
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Testing and evaluating model performance using benchmarks
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Deploying models for real-time or batch inference
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Collaborating with software engineers to integrate models into applications
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Annotating datasets and managing data pipelines
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Monitoring edge-case performance and refining algorithms
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Documenting methodologies, experiments, and model assumptions
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Staying up to date on advancements in computer vision research
This role blends deep learning, data engineering, and real-world application.
Skills and requirements for a computer vision engineer.
Computer vision engineers build systems processing visual data. Employers typically look for:
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3–6 years of experience in machine learning or computer vision
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Strong programming skills in Python, OpenCV, or TensorFlow
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Experience with object detection, segmentation, or facial recognition
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Skilled in data preprocessing, annotation, and augmentation
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Understanding of deep learning models (CNNs, YOLO)
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Experience deploying models into production
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Familiarity with GPU acceleration and model tuning
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Strong mathematical and analytical ability
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Portfolio demonstrating applied computer vision projects
Most computer vision engineers work on automation, analysis, or visual data classification.
Average salary for a computer vision engineer.
In the UK, the average salary for a computer vision engineer typically ranges from £50,000 to £75,000, depending on algorithm development, machine learning, and industry application.
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Mid-level engineers tend to earn between £50,000 and £62,000
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Senior engineers working on edge devices, robotics, or deep learning models may earn between £63,000 and £75,000
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Specialists with academic research and commercial implementation experience are in high demand
Top salaries are offered in AI startups, medtech, and autonomous systems development.
Career progression for a computer vision engineer.
A computer vision engineer builds systems that process and interpret visual data — from facial recognition to object detection. This is a specialised AI role with strong technical depth and growth potential. A typical career path includes:
ML engineer
Supports model training, data labelling, and testing of image-based algorithms.
Computer vision engineer
Develops vision models using CNNs and OpenCV. Works on tasks like detection, tracking, segmentation, and recognition.
Senior engineer / AI specialist
Leads research and development for complex vision projects. Optimises for accuracy, latency, and scale.
Computer vision architect
Designs vision pipelines, manages product deployment, and collaborates with cross-functional AI teams.
Head of AI
Leads vision innovation. Aligns projects with commercial goals and emerging technologies.
salary guide
Our UK data salary guide.
Computer vision engineers build systems that interpret images and video. Compensation should reflect their experience with models, algorithms, and real-time processing.
Our UK data salary guide includes specialist benchmarks, 2024 data comparisons, hiring insight, and projections to 2026.
FAQS
Computer vision engineer FAQs.
Beyond universities and research labs, computer vision engineers are hired by medtech firms (e.g. radiology imaging), autonomous vehicle developers, retail analytics companies (e.g. CCTV to footfall tracking), and AR/VR platforms. Startups using real-time image processing for manufacturing or sports tech are also growing rapidly.
Hiring based solely on model knowledge rather than deployment experience. Many candidates have built object detection models, but few have optimised inference for edge devices or real-time processing — which is what product teams need.
Not anymore. While research roles may still require academic credentials, commercial teams prioritise candidates with strong Python skills, experience with OpenCV or YOLO, and knowledge of deploying models using TensorRT, ONNX, or AWS SageMaker.
Data quality and generalisability. Many engineers find that their training data doesn’t reflect production conditions (e.g. lighting, motion blur, varied angles), and performance drops sharply. Robustness matters more than leaderboard metrics.
Engineers may grow into roles like applied ML lead, perception architect, or even transition into robotics ML roles. Those with strong deployment skills often lead platform optimisation or MLOps for CV systems.
