Machine Learning Engineer KTP Associate
| Posting date: | 23 May 2025 |
|---|---|
| Hours: | Full time |
| Closing date: | 22 June 2025 |
| Location: | Salford, M5 4WT |
| Company: | University of Salford (Personnel) |
| Job type: | Contract |
| Job reference: | 44 |
Summary
Opportunity Overview
This is an exciting opportunity for an ambitious Machine Learning Engineer to fast-track their career development as a Knowledge Transfer Partnership (KTP) Associate. The successful candidate will undertake a 36-month collaborative project between Corelain and The University of Salford.
The Knowledge Transfer Partnership scheme is one of the UK's largest graduate recruitment schemes (http://ktp.innovateuk.org/) and is a three-way collaborative project between the Associate, Corelain and the University of Salford. It provides an opportunity for the successful applicant to manage a challenging project central to Corelain’s strategic development and long-term growth.
This post is a dynamic role that will lead a unique innovation project aiming to develop a digital surveyor tool (DST) focused on streamlining building defect identification, remediation and retrofit design solutions.
The DST will be scalable and user-friendly platform that will boost the capacity and capabilities of building managers and disrepair/retrofit surveyors, improving performance and enabling the management of larger portfolios.
The position will be based at the company premises in Central Manchester, (M3) working as part of the Building Surveying Team, however the successful candidate should be in a position to travel to the University of Salford as required for meetings on a regular basis. In addition, you will gain experience in engaging with colleagues and stakeholders from across the entire organisation and beyond. This experience will uniquely position the candidate to have wide-ranging knowledge and experience.
What will you be doing?
This project presents several challenges, offering significant opportunities for personal development.
You will need to enhance your expertise in ML, AI, and computer vision, particularly in processing multimodal datasets to develop an AI-powered digital surveyor tool that accurately detects building defects. This will be a complex task, requiring proficiency in programming languages like Python and the application of advanced algorithms.
You will manage a strategically important project for Corelain and be required to manage stakeholder expectations. You will have excellent communications skills and use these to influence and navigate any resistance from users arising from implementing a highly technical solution to replace more traditional methods.
Who are we looking for?
- A graduate with a minimum 2.1 BEng/BSc in in Computer Science/Data Science/related subject.
- Knowledge and experience of applying AI&ML to create highly innovative products capable of carrying out complex tasks.
To attract the highest level of candidate, they may have the opportunity to register for a higher degree (fees waived) to further their development. Please see the Job Description and Person Specification for full details.
Additional added benefits
To facilitate professional development the successful candidate will receive extensive practical and formal training, gain highly desirable specialist business skills, broaden knowledge and expertise within an industrially relevant project, and gain valuable experience from their industry and academic mentors. The KTP Associate will also benefit from a Personal Development Budget of £6,000 (over and above their salary) and might have the opportunity to register free of charge for a further higher degree MPhil/PhD.
At the University of Salford we are committed to an inclusive approach to promoting equality and diversity. We aim to have a more diverse workforce at all levels of the institution and particularly welcome applications from people from minority ethnic backgrounds and people with disabilities, who are under-represented in our workforce.
Job Description
Proud member of the Disability Confident employer scheme