TL;DR
Machine Learning Engineer (Computer Vision): Designing, training, and optimizing computer vision models for vehicle damage detection with an accent on improving model accuracy, precision, and recall. Focus on structured evaluation, error analysis, and production deployment of models.
Location: UK Remote / Hybrid
Salary: £65,000 - £85,000 per annum
Company
hirify.global is a growing technology company focused on real-world AI applications.
What you will do
- Design, train, and optimise computer vision models for vehicle damage detection using object detection and segmentation approaches.
- Improve model accuracy, precision, and recall across priority damage categories through structured evaluation and retraining.
- Work closely with data and annotation teams to define damage classes and address class imbalance.
- Carry out error analysis to understand false positives and negatives and drive targeted model improvements.
- Own evaluation datasets, metrics, and performance reporting across training, validation, and test sets.
- Collaborate with MLOps and platform teams to package, deploy, and monitor models in production.
Requirements
- Strong experience in machine learning focused on computer vision.
- Hands-on experience training and deploying object detection or segmentation models such as YOLO or similar architectures.
- Proficiency in Python and common ML and computer vision libraries.
- Experience working with large image datasets and noisy real-world data.
- Ability to translate operational or business problems into measurable ML objectives.
- Comfortable working in an iterative, delivery-focused engineering environment.
Culture & Benefits
- Ownership of an end-to-end ML problem with real operational impact.
- Opportunity to build and scale production computer vision systems.
- Work with cross-functional teams spanning data, operations, and platform engineering.
- Exposure to complex, real-world datasets rather than synthetic or lab-only use cases.
- Long-term growth as part of an expanding AI capability.
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