Data Annotation & Tooling Engineer

We’re looking for a Data Annotation & Tooling Engineer to help build the high-quality datasets that underpin cutting-edge computer vision and AI systems.

This is more than a traditional data annotation role. You’ll be responsible for developing and improving annotation workflows, maintaining tooling, ensuring dataset quality, and working closely with machine learning engineers to create reliable training data for advanced computer vision models.

You’ll play a key role in ensuring data quality throughout the machine learning lifecycle, helping improve model accuracy by creating robust, well-structured datasets. Responsibilities

• Prepare, curate and annotate large-scale image and video datasets.
• Develop and improve annotation workflows and tooling.
• Configure and maintain annotation platforms.
• Create annotation guidelines and documentation.
• Validate dataset quality and consistency.
• Perform quality assurance on annotated datasets.
• Work closely with Machine Learning Engineers to understand model requirements.
• Identify labelling inconsistencies and improve annotation standards.
• Assist with dataset versioning and management.
• Automate repetitive annotation and validation tasks where possible using Python.

• Experience working with computer vision datasets.
• Experience using annotation tools.
• Strong attention to detail.
• Python scripting experience.
• Comfortable handling large datasets.
• Understanding of image classification, object detection and segmentation tasks.
• Experience creating clear documentation and annotation guidelines.
• Excellent organisational skills.

Desirable

• Experience with PyTorch datasets.
• Experience with MLOps or data pipelines.
• Understanding of computer vision model training.
• Knowledge of active learning workflows.
• Experience with Git.
• Robotics or autonomous systems experience.

What You’ll Be Doing

You’ll work directly with the AI and Computer Vision engineers, helping build the datasets that power next-generation autonomous systems. The role combines technical problem-solving with meticulous attention to detail and offers the opportunity to influence how AI systems are trained and evaluated.