Data Tooling Engineer - Computer Vision

We’re looking for a Data Tooling Engineer to develop the internal tools and workflows used to create, manage and quality-check the datasets underpinning advanced computer vision and AI systems.

This is primarily a tooling and engineering role rather than a traditional data annotation position. You’ll build and improve Python-based tools, automate repetitive processes and help create efficient, reliable workflows for working with large-scale image and video datasets.

Working closely with Machine Learning and Computer Vision Engineers, you’ll translate operational and technical requirements into practical tooling that improves annotation efficiency, data quality and the overall reliability of the machine learning pipeline.

This could suit someone with experience in data tooling, pipeline development, workflow automation, technical art or internal software development who is interested in applying those skills within computer vision and autonomous systems. Responsibilities

• Design, develop and maintain internal tools supporting the creation and management of computer vision datasets.
• Build Python-based applications, scripts and automation to improve annotation and data-processing workflows.
• Develop intuitive tools and interfaces for technical and non-technical users.
• Integrate annotation platforms and internal systems using APIs and other appropriate technologies.
• Automate repetitive data preparation, validation and quality-assurance processes.
• Work with large-scale image, video and associated metadata.
• Identify workflow bottlenecks and develop technical solutions to improve efficiency and reliability.
• Implement validation and quality-control processes to identify incomplete, inconsistent or incorrectly structured data.
• Support dataset organisation, versioning, transformation and export.
• Troubleshoot tooling, integration and data-processing issues.
• Work closely with Machine Learning and Computer Vision Engineers to understand dataset and model requirements.
• Gather feedback from users and continually improve tooling and workflows.
• Produce clear technical documentation and user guidance.
• Contribute to appropriate testing, code review and deployment practices.

• Commercial experience developing tools, applications or workflow automation using Python.
• Experience designing technical solutions that improve operational or production workflows.
• Experience working with APIs, systems integrations or data-transfer processes.
• Confidence handling structured data, files and metadata.
• An understanding of software-engineering fundamentals, including maintainable code, testing, debugging and version control.
• Experience gathering requirements and building practical tools for end users.
• Strong problem-solving skills and the ability to investigate complex technical or data-related issues.
• The ability to communicate effectively with both technical and non-technical colleagues.
• Strong attention to detail, particularly around data accuracy and reliability.

Desirable Experience

• Experience working with image, video or other visual datasets.
• Experience developing user interfaces, particularly using PyQt or similar technologies.
• Familiarity with data annotation platforms or annotation workflows.
• Understanding of image classification, object detection or segmentation.
• Experience with data pipelines, dataset versioning or MLOps practices.
• Familiarity with SQL or database technologies.
• Experience working with cloud-based data or storage platforms.
• Knowledge of PyTorch datasets or computer vision model-training workflows.
• Understanding of active learning or human-in-the-loop systems.
• Experience using Git and collaborative development workflows.
• Experience within robotics, autonomous systems, VFX, animation, gaming or another image-intensive technical environment.

What You’ll Be Doing

You’ll work directly with AI and Computer Vision Engineers to build the tools and workflows that support next-generation autonomous systems.

Your work will make it easier to prepare, review, validate and manage the data used to train and evaluate machine learning models. The role combines hands-on Python development, systems integration, user-focused tooling and technical problem-solving, with the opportunity to influence how sophisticated computer vision systems are developed.