<p>A skilled Computer Vision Engineer is required to design, develop and deploy image and video analysis systems for real-world applications. The successful candidate will work across research and engineering teams to deliver robust, optimised models into production. This role suits someone with strong machine learning foundations, practical experience in deep learning frameworks and a track record of deploying models at scale. A pragmatic problem solver with excellent communication skills will thrive in this position.</p>
<p>This job description outlines the primary purpose, core responsibilities and essential qualifications for a Computer Vision Engineer. Use this template to identify and attract top candidates who can advance your computer vision initiatives.</p>
<h2>Computer Vision Engineer Job Profile</h2>
<p>The Computer Vision Engineer designs algorithms and models to extract meaningful information from images and video. They translate research concepts into production-quality code and collaborate with data engineers, software developers and product teams.</p>
<p>Typical work includes prototype development, dataset curation, training and validating deep learning models, and integrating inference code within scalable pipelines. The role balances innovation with practical considerations such as latency, accuracy and maintainability.</p>
<h2>Computer Vision Engineer Job Description</h2>
<p>As a Computer Vision Engineer, you will lead the end-to-end delivery of computer vision features. You will analyse business problems, select appropriate modelling approaches and implement solutions that meet performance and reliability targets. Your day-to-day tasks will cover data preparation, model selection, training, hyperparameter tuning, and rigorous evaluation against defined metrics.</p>
<p>In addition to model development, you will be responsible for optimisation and deployment. This includes converting models for efficient inference, applying quantisation or pruning where suitable, and integrating models into microservices or edge devices. You will contribute to test and validation strategies, monitor model performance in production and iterate to reduce bias and drift.</p>
<p>The role also requires clear documentation and knowledge transfer. You will write technical specifications, create reproducible training pipelines and mentor junior engineers. Collaboration with research teams may involve publishing technical reports or contributing to open-source projects to advance the company’s capabilities.</p>
<h2>Computer Vision Engineer Duties and Responsibilities</h2>
<ul>
<li>Design and implement computer vision algorithms for image and video analysis.</li>
<li>Create and manage labelled datasets, and develop augmentation strategies to improve generalisation.</li>
<li>Build, train and evaluate deep learning models such as CNNs, Transformers and hybrid architectures.</li>
<li>Optimise models for inference speed and memory usage to meet deployment constraints.</li>
<li>Integrate models into production systems, including cloud services and edge devices.</li>
<li>Collaborate with cross-functional teams to define requirements, success metrics and deployment plans.</li>
<li>Develop robust evaluation pipelines and monitor model performance and data drift in production.</li>
<li>Maintain reproducible code, write unit and integration tests, and document methods and experiments.</li>
<li>Stay current with state-of-the-art techniques and propose research directions or proof of concepts.</li>
<li>Provide technical guidance and mentor junior engineers and data scientists.</li>
</ul>
<h2>Computer Vision Engineer Requirements and Qualifications</h2>
<ul>
<li>Bachelor's or Master's degree in Computer Science, Engineering, Mathematics or related discipline; PhD desirable for research-focused roles.</li>
<li>Proven experience in computer vision, image processing or machine learning roles.</li>
<li>Strong programming skills in Python and familiarity with libraries such as OpenCV, scikit-image and NumPy.</li>
<li>Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow and model training on GPUs.</li>
<li>Experience with model optimisation techniques, including quantisation, pruning and knowledge distillation.</li>
<li>Familiarity with deployment tools and platforms such as Docker, Kubernetes, TensorRT or ONNX Runtime.</li>
<li>Solid understanding of data annotation processes, labelling tools and quality assurance for datasets.</li>
<li>Good knowledge of software engineering best practices, including version control, CI and testing.</li>
<li>Excellent analytical and communication skills with the ability to explain complex ideas to non-technical stakeholders.</li>
<li>Experience with video analytics, 3D reconstruction, SLAM or specialised domains is an advantage.</li>
</ul>