Machine Learning Engineer

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Job Location: Milton Keynes, Remote/Hybrid

Department: Data & Student Analytics

Closing Date: 5 September 2025

Weekly Working Hours: 37

Contract Type: Permanent

Fixed Term Contract: End Date: Not Applicable

Welsh Language: Not Applicable

Job Description: About the Role

We believe that where you start in life, shouldn't limit where you go.

The Open University is a unique institution created 50 years ago to open up education for all. We are open to anyone, anywhere, changing millions of lives across the world and always looking for new ways to help people succeed. Our pioneering use of innovative methods, such as our BBC partnership, means we reach more people than any other university.

We work across all four nations of the UK and we are the most popular choice for online, distance, flexible study.

This Machine Learning (ML) Engineer role forms part of the Data & Student Analytics team within Digital Services working within a team who are responsible for engaging with end-users in identifying, developing, and deploying the suite of models required to enable effective planning and support students within The Open University. This will be delivered using advanced data science models deployed on the principles of MLOps techniques and approaches.

• Maintain and develop data and ML pipelines integrating AI/ML, and other services, into large-scale production applications
• Improve efficiency and reduce manual effort across data and ML engineering workflows, using scripting (e.g. bash and python) to automate and streamline processes
• Work in a cross-functional team of Machine Learning engineers and Data Scientists to design and code large scale batch and real-time ML pipelines
• Perform code reviews to optimise technical performance of data science solutions.
• Build and deploy predictive models and machine learning algorithms.
• Ensure that output of the developed products is actionable and adds value.
• Take responsibility for projects & lead them without active supervision. Independently research new tools and approaches to identify the most appropriate solutions.
• Able to tell a compelling story by making complex items simple to understand for technical and non-technical audiences.

• Programming experience in Python, using Azure DevOps, or other CI/CD tools, and Git, preferably in a commercial setting.
• Experience with Apache Spark is desirable.
• Knowledge & experience working with cloud platforms for the deployment of machine learning solutions such as Microsoft Azure Machine Learning Studio.
• Experience with Bash scripting for task automation in Linux/Unix environments
• A degree or equivalent experience in a numerical subject such as Data Science, Computer Science, Mathematics, Engineering.
• Experience with contributing to the design and architecture of Machine Learning products.
• Previous experience of owning, maintaining, and enhancing software products used by data scientists, and supporting data scientists in their development needs.
• Knowledge of Data Science techniques with evidence of using them on data across supervised & unsupervised learning business problems.
• Experience in practical data processing, data visualization & information retrieval.

Personal Qualities and abilities

• Take a proactive approach to work and be flexible and adaptable to meet the changing demands of the unit and the University.
• Ability to learn and adapt to new technologies and software as working in area of rapid technological development.
• Motivation to implement solutions that follow best practice and promote best practice MLOps principles.
• Pay close attention to detail in all aspects of work to ensure a high standard of output
• Ensure that all relevant data protection, information security and data retention policies are adhered to.
• Demonstrate a strong commitment to the principles and practice of equality and diversity
• Excellent communication and presentation skills, both oral and written
• Able to work independently, prioritise workload, work well under pressure and respond to change in a flexible and positive manner
• Working knowledge of using Microsoft Office applications, such as Outlook, Work and Teams.
• Undertake any other duties which may reasonably be required.

As part of the application process, you will be expected to submit your CV and a Supporting Statement (maximum of 1000 words) that demonstrates how you meet the essential / desirable criteria listed above.

We are open to discussions about flexible working, whether it's a job share, part time, compressed hours or another working arrangement. Please reach out to us to discuss what may work for you and the role.

It is anticipated that a hybrid working pattern can be adopted for this role, where the successful candidate can work from home and the office. However, as this role is contractually aligned to our Milton Keynes office it is expected that some attendance in the office will be required when necessary and in response to business needs. We anticipate this being once per month on average.

Please Note: We may close this job advert earlier than the published closing date where a satisfactory number of applications are received. We would therefore encourage early applications.

If you have any queries or questions about the recruitment process, or regarding your application,please contact: .

Please use our AL home page to find AL vacancies.

The Open University is committed to equality, diversity and inclusion which is reflected in our mission to be open to people, places, methods and ideas. We aim to foster a diverse and inclusive environment so that all in our OU community can reach their potential. We recognise that different people bring different perspectives, ideas, knowledge, and culture, and that this difference brings great strength.We strive to recruit, retain and develop the careers of a diverse pool of students and staff, and particularly encourage applications from all underrepresented groups. We also aspire to make The Open University a supportive workplace for all through our policies, services and staff networks.