Machine Learning - Computer Vision in Leicester

Machine Learning Engineer – Computer Vision

A fast-growing AI safety company is hiring Machine Learning Engineers with strong experience in computer vision, object detection, and object tracking. You’ll work across applied ML, customer delivery, and product R&D to improve how real-world models are tested, validated, and deployed.

In this role, you will help customers in aviation, mobility, robotics, and edge/embedded systems validate and optimise their computer vision pipelines. You’ll also work closely with internal research and product teams to shape the next generation of ML validation tools.

What You’ll Do

• Evaluate, test, and improve CV models using modern architectures (YOLO, ViTs, EVA, etc.).
• Run experiments, benchmark performance, and analyse results across different datasets.
• Build prototypes and tools for robustness, validation, and ML model assessment.
• Work with customer engineering teams to understand challenges and deliver solutions.
• Collaborate with internal research/product teams to guide roadmap and platform features.

About You

You’re an ML engineer or researcher with practical experience training, evaluating, and deploying computer vision models. You can communicate technical concepts clearly and enjoy working on applied problems.

Key Requirements

• Strong experience with object detection/tracking and vision architectures.
• Deep knowledge of Python, PyTorch, TensorFlow, and core ML tools (NumPy, pandas, scikit-learn).
• Experience with evaluation metrics (Accuracy, Recall, F1, IoU).
• Strong problem-solving and communication skills.

Bonus:

• Background in robotics, aviation, mobility, or edge computing.
• Experience deploying ML solutions in real-world environments.

Why Join

• Work on real-world AI safety challenges with global impact.
• Influence product direction and research decisions.
• Competitive salary + stock options, strong L&D support, flexible working, and a collaborative culture.

If you’re passionate about advancing computer vision and want your work to contribute to safer, more reliable AI systems, we’d love to hear from you.