Data Annotator – Contribute to Leading Ecological AI Research Programme

Data Understood is recruiting Data Annotators across the United Kingdom to contribute to a high-quality ecological artificial intelligence research initiative delivered in collaboration with a leading scientific institution.

The work involves helping build a large-scale training dataset used to support advanced computer vision models analysing biological microscopy imagery.

This is applied AI in practice. The work completed by the annotation team will contribute to the creation of a robust machine learning dataset used to support model training and validation in a research-grade environment.

This role is suited to people who are comfortable working carefully and consistently with detailed scientific imagery.

Successful candidates will support the classification of high-resolution microscopy images using structured guidance and a clearly defined quality framework. The work will contribute to the development of high-quality AI training data used in a serious ecological research context.

This is not casual tagging work. It is structured, quality-controlled annotation that plays an important part in the development of a production-grade machine learning dataset.

• Classify high-resolution microscopy imagery
• Apply structured biological classification guidance
• Support the creation of high-quality AI training datasets
• Work within a two-reviewer validation process
• Maintain accuracy and consistency across annotation work
• Participate in calibration and quality review sessions
• Contribute to a research-grade computer vision workflow

We are particularly interested in candidates who combine scientific curiosity with strong attention to detail and the ability to work accurately within a structured framework.

• Biology
• Ecology
• Environmental science
• Plant science
• Microbiology
• Scientific research
• Closely related disciplines

• Strong attention to detail
• Ability to follow structured guidance
• Comfortable working on image-based analytical tasks
• Consistent and accurate working style
• Good written communication
• Ability to work independently within a quality-controlled process

Prior experience in AI or machine learning is not required. Full onboarding and guidance will be provided.

The project operates within a structured annotation framework designed to ensure quality, consistency and reliability.

This includes a dual-review workflow, defined validation standards, calibration sessions and ongoing quality assurance checks. The objective is to produce a dataset that meets the standards required for modern machine learning model development.

Due to the nature of the work, appropriate data governance and confidentiality standards apply.

Successful candidates may be required to sign a short confidentiality and data handling agreement before onboarding.

All project work must be carried out only within approved systems and in line with the project's handling requirements. Protecting data integrity, confidentiality and quality is an essential part of the role.

This is a strong opportunity for candidates who want exposure to real-world AI delivery, scientific data workflows and research-grade quality standards.

• AI training dataset development
• Computer vision support workflows
• Structured annotation and validation
• Quality-controlled research delivery
• Applied machine learning support in a live project environment