Matchtech
Computer Vision Engineer
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Machine Learning Engineer (Computer Vision)
Location: Surrey or Bristol (hybrid)
Type: Permanent (full-time)
Team: AI / Computer Vision
Visa: Unfortunately, visa sponsorship is not available for this role.
About the Company
We're a UK-based technology business building AI-led vision systems that turn image and video streams into automated, real-time insight. Our products are used in operational environments where speed, reliability, and accurate detection matter—helping teams monitor large areas and respond quickly when something changes.
The Role
We're looking for a Machine Learning / Computer Vision Engineer to help tackle challenging real-world problems using modern deep learning. You'll work closely with a multidisciplinary team to develop, productionise, and deploy computer vision models that run reliably across cloud and edge environments.
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What You'll Be Doing
- Build, test, and improve production-grade deep learning models for computer vision tasks (classification, detection, segmentation, tracking)
- Optimise and deploy CV/ML pipelines to cloud and edge platforms
- Design, curate, and manage image/video datasets for training and evaluation
- Develop and maintain annotation workflows and tooling
- Create synthetic data pipelines (including generative approaches) to augment real-world datasets
- Stay current with emerging tools, methods, and best practices in CV/ML
What We're Looking For
- Degree in Computer Science, Electrical Engineering, Robotics (or similar), or equivalent commercial experience
- Proven experience delivering deep learning solutions for core CV tasks (classification / detection / segmentation / tracking)
- Strong skills with PyTorch and CV libraries such as OpenCV / scikit-image
- Strong, production-ready Python engineering (version control, testing, code reviews)
- Analytical mindset with strong problem-solving ability
- Clear communicator who works well in a collaborative team
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Nice to Have
- Experience with real-world sensor data (e.g., RGB-D, thermal, radar)
- Model optimisation/deployment tooling (ONNX, TensorRT)
- Edge deployment experience (e.g., NVIDIA Jetson or other resource-constrained devices)
- Familiarity with MLOps tooling (e.g., DVC, MLflow)
- Relevant open-source contributions in computer vision
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