Computer Vision Engineer
Role details
Job location
Tech stack
Job description
As a Computer Vision Engineer, you will contribute to the design, development, and deployment of production-grade computer vision systems, supporting both intelligence generation and autonomous action. You will work in multidisciplinary teams alongside engineers, consultants, and domain experts, transforming complex technical problems into robust, reusable, and maintainable solutions with real-world impact.
Your role
You will be involved across the full computer vision lifecycle, from problem definition through to deployment and continuous improvement.
- Problem framing & solution design: Translate product, robotics, or inspection needs into well-scoped computer vision tasks and end-to-end processing pipelines.
- Data pipelines: Plan and execute data collection, curation, augmentation, and annotation strategies for computer vision datasets.
- Modelling & algorithms: Implement, adapt, and fine-tune computer vision models and methodologies, spanning both 2D and 3D techniques.
- Model & pipeline evaluation: Define and implement appropriate performance metrics (accuracy, robustness, latency, efficiency) and carry out structured error and failure-mode analysis.
- Optimisation & deployment: Optimise models and pipelines for deployment on edge and accelerated platforms, balancing accuracy, latency, resource usage, and reliability.
- Integration & productionisation: Build and maintain production-grade inference and processing services suitable for diverse deployment environments. Use version control, CI/CD, and dataset/model versioning to support reproducible and maintainable delivery.
Requirements
- Proficient in Python and / or C++ with hands on experience with tools such as PyTorch and OpenCV.
- Understanding and proven experience utilising a wide range of computer vision algorithms including:
- Classical deterministic image processing.
- 2D based deep learning including object detection and similar processes.
- 3D scene reconstruction and spatial reasoning.
- Image translation alignment and warping.
- Experience managing the full model lifecycle: training, validation, tuning, deployment, maintenance and iterative improvement.
- Understanding of hardware acceleration for development and deployment, including technologies such as CUDA, DirectML, and ONNX execution providers.
- Solid experience using Git (or equivalent) and working within CI/CD pipelines.
- Ability to produce clear, structured technical documentation, including design decisions, data and model lineage, evaluation results, and operational guidance, suitable for both engineering and non-specialist stakeholders.
- Experience using open-source data labelling tools (e.g. Label Studio or similar).
- Experience integrating multi-modal vision data, including:
- Standard 2D imagery (colour and grayscale).
- Depth sensing technologies.
- Hyperspectral imagery.
- X-ray and gamma imaging.
Bonus Skills That Help You Thrive. These aren't required, but they'll help you make an even greater impact:
- Embedded and edge acceleration, including CUDA, cuDNN, and TensorRT.
- Advanced 3D representations such as NeRFs and Gaussian Splatting.
- Experience with 3D visualisation or simulation engines (e.g. NVIDIA Omniverse, Unity, Unreal).
- Model explainability techniques (e.g. class activation maps).
- CI/CD-based testing of ML and computer vision pipelines.
- Understanding of open-source licensing and its implications in commercial and regulated environments.
Benefits & conditions
Explore the rewards and benefits that help you thrive - at every stage of your life and your career. Enjoy competitive salaries, employee rewards and a brilliant range of benefits you can tailor to suit your own health, wellbeing, financial and lifestyle choices. Make the most of a myriad of opportunities for training and professional development to grow your skills and expertise. And combine our hybrid working culture and flexible holiday allowances to balance a great job and fulfilling personal life.