Machine Learning Engineer

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