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.
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 (or similar) 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
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