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AI / Machine Learning Engineer (KTP Associate)

University of Derby - College of Science and Engineering

Location: Derby
Salary: £35,000 to £36,600 per annum (for exceptional performers, there is scope for further progression up to £39,000 per annum)
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 13th August 2026
Closes: 13th September 2026
Job Ref: 0212-26

Contract Type: Fixed Term – 26 months

ZapCarbon is an award-winning UK environmental innovation company that develops low-carbon technologies and data-driven solutions to improve energy efficiency, reduce carbon emissions, and create healthier homes. Since 2006, the company has worked with public and private sector organisations to deliver practical innovations that support sustainability, energy optimisation, and net-zero ambitions.

About the Role

Based at ZapCarbon’s premises in Leighton Buzzard, you will be employed by the University as a KTP Associate but work under the terms and conditions of the company.

You will lead a major innovation programme, developing and delivering a scalable, AI-driven system that enables housing providers to better interpret and act on large-scale building data, improving maintenance decision-making, reducing costs and supporting the decarbonisation of housing stock. 

You will take ownership of the full project lifecycle from acquiring and integrating multiple data sources, developing and validating machine learning models, building data pipelines and dashboards, and translating technical outputs into actionable insights for operational and strategic stakeholders.

Knowledge Transfer Partnerships 

This full-time post is part-funded by the UK Government’s KTP programme. A KTP is a three-way project between a graduate, an organisation and a university. KTP is one of the UK’s largest graduate recruitment programmes and has been placing graduates on challenging, high profile projects for 50 years. In addition to a competitive salary and core KTP development training, you will have a dedicated budget of £4,000 further training and career development. To find out more about the scheme visit: ktp-uk.org/graduates

Please note by completing an application form for this role, you are giving your consent for us to share your personal data with the KTP partner.

About You

You will hold a Master's degree in Data Science, Artificial Intelligence, Computer Science or a closely related discipline, or a Bachelor's degree in a related field backed by substantial relevant experience, and you will bring proven experience of developing machine learning models using complex, multi-source datasets.

Strong programming skills in Python, alongside proficiency in SQL, are essential, as is a solid understanding of machine learning workflows, model validation and performance evaluation. You will be comfortable designing scalable analytical solutions, integrating data from a variety of sources (including sensor data), and working with visualisation tools such as Power BI. 

We are particularly interested in candidates who have a technical foundation in machine learning and data engineering, and a genuine interest in applying your expertise to sustainability.

Important Information

This role may be eligible for sponsorship by the University. 

The offered salary for this role is less than the going rate for the occupation and the minimum salary threshold for the Skilled Worker route (£41,700 per annum, as of 22nd July 2025). Therefore you will only be eligible to apply for a Skilled Worker visa subject to your individual circumstances which must meet one of the following criteria set out by UKVI

  • Your job is on the Immigration Skills List
  • You’re under 26, studying or a recent graduate, or in professional training
  • You have a PhD level qualification that’s relevant to your job 
  • You have a postdoctoral position in science or higher education

You must also meet the English Language requirement.

Please note that the University will assess your individual eligibility for sponsorship at the shortlisting stage. 

For more information and to apply on-line, please click the “Apply” button above. 

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