Machine Learning Engineer Apprentice
Science and Technology Facilities Council
Oxfordshire (OX11 0QX)
Closes in 24 days (Monday 2 February 2026)
Posted on 6 January 2026
Contents
Summary
Join us in our Particle Physics department supporting world-class research and innovation. A unique opportunity to contribute to real projects in data science, AI, and high-performance computing. If you're curious, analytical, and ready to build a career at the intersection of software and science, we’d love to hear from you.
- Wage
-
£24,340 a year
Check minimum wage rates (opens in new tab)
Your salary will increase annually as you progress through your apprenticeship, in line with policy.
- Training course
- Machine learning engineer (level 6)
- Hours
-
Monday to Friday 9am to 5pm
37 hours a week
- Start date
-
Thursday 3 September 2026
- Duration
-
2 years
- Positions available
-
1
Work
Most of your apprenticeship is spent working. You’ll learn on the job by getting hands-on experience.
What you'll do at work
Key responsibilities include:
- Communicate and work with fellow team members on a daily and weekly basis
- Take an active role in meetings
- Present progress in slides to update team members (small group) at regular intervals, weekly or bi-weekly, both onsite and occasionally in conference calls
- Analyse data in order to design machine learning algorithms
- Write documentation/technical notes to document the design of the algorithm
- Use a variety of tools and technologies and coding language(s) used by the team to develop the machine learning algorithms
- Show initiative especially regarding learning new things
- Participate in the wider department and STFC apprentice training programme
- Work independently at times and ask questions if unsure
- Take responsibility and aim to deliver work of a high standard
Where you'll work
Rutherford Appleton Laboratory
Harwell
Didcot
Oxfordshire
OX11 0QX
Training
Apprenticeships include time away from working for specialist training. You’ll study to gain professional knowledge and skills.
Training provider
TECHNICAL PROFESSIONALS LIMITED
Training course
Machine learning engineer (level 6)
Understanding apprenticeship levels (opens in new tab)
What you'll learn
Course contents
- Assess vulnerabilities of the proposed design, to ensure that security considerations are built in from inception and throughout the development process.
- Translate business needs and technical problems to scope machine learning engineering solutions.
- Select and engineer data sets, algorithms and modelling techniques required to develop the machine learning solution.
- Apply methodologies and project management techniques for the machine learning activities.
- Create and deploy models to produce machine learning solutions.
- Document the creation, operation and lifecycle management of assets during the model lifecycle.
- Apply techniques for output model testing and tuning to assess accuracy, fit, validity and robustness.
- Assess system vulnerabilities and mitigate the threats or risks to assets, data and cyber security.
- Refine or re-engineer the model to improve solution performance.
- Apply techniques for monitoring models in the live environment to check they remain fit for purpose and stable.
- Consider the associated regulatory, legal, ethical and governance issues when evaluating choices at each stage of the data process.
- Apply machine learning and data science techniques to solve complex business problems.
- Track and test continual learning models.
- Analyse test data, interpret results and evaluate the suitability of proposed solutions both new and inherited models, considering current and future business requirements.
- Identify, consider and advocate for ML solutions to deliver an environmental and operational sustainable outcome.
- Transition prototypes into the live environment.
- Complete audit activities in compliance with policies, governance, industry regulation and standards.
- Consider the risks with using digital and physical supply chains.
- Ensure the model capacity is scaled in proportion to the operating requirements.
- Support the evaluation and validation of machine learning models and statistical evidence to minimise algorithmic bias being introduced.
- Monitor data curation and data quality controls including for synthetic data.
- Identify and select the machine learning or artificial intelligence platform architecture and specific hardware, to contribute to solving a computational problem using allocated resources.
- Identify and embed changes in work to deliver sustainable outcomes.
- Monitor model data drift, using performance metrics to ensure systems are robust when moving outside of their domain of applicability.
- Develop a process to decommission assets in line with policy and procedures. Manage current and legacy models in line with industry approaches.
- Undertake independent, impartial decision-making respecting the opinions and views of others in complex, unpredictable and changing circumstances.
- Coordinate, negotiate with and manage expectations of diverse stakeholders suppliers and multi-disciplinary teams with conflicting priorities, interests and timescales.
- Produce and maintain technical documentation explaining the data product, that meets organisational, technical and non-technical user requirements, retaining critical information.
- Create and disseminate reports, presentations and other documentation that details the model development to confirm stakeholder approval for handover to implementation.
- Comply with equality, diversity, and inclusion policies and procedures in the workplace.
- Horizon scan to identify new technological developments that offer increased performance of data products.
- Apply Machine Learning principles and standards such as, organisational policies, procedures or professional body requirements.
- Integrate AI-based approaches, including those provided by third-party vendors’ Application Programming Interfaces, into existing and new processes.
- Proactive identification of the potential for automation for example through AI solutions embedded within tooling.
- Assess vulnerabilities of the proposed design, to ensure that security considerations are built in from inception and throughout the development process.
- Translate business needs and technical problems to scope machine learning engineering solutions.
- Select and engineer data sets, algorithms and modelling techniques required to develop the machine learning solution.
- Apply methodologies and project management techniques for the machine learning activities.
- Create and deploy models to produce machine learning solutions.
- Document the creation, operation and lifecycle management of assets during the model lifecycle.
- Apply techniques for output model testing and tuning to assess accuracy, fit, validity and robustness.
- Assess system vulnerabilities and mitigate the threats or risks to assets, data and cyber security.
- Refine or re-engineer the model to improve solution performance.
- Apply techniques for monitoring models in the live environment to check they remain fit for purpose and stable.
- Consider the associated regulatory, legal, ethical and governance issues when evaluating choices at each stage of the data process.
- Apply machine learning and data science techniques to solve complex business problems.
- Track and test continual learning models.
- Analyse test data, interpret results and evaluate the suitability of proposed solutions both new and inherited models, considering current and future business requirements.
- Identify, consider and advocate for ML solutions to deliver an environmental and operational sustainable outcome.
- Transition prototypes into the live environment.
- Complete audit activities in compliance with policies, governance, industry regulation and standards.
- Consider the risks with using digital and physical supply chains.
- Ensure the model capacity is scaled in proportion to the operating requirements.
- Support the evaluation and validation of machine learning models and statistical evidence to minimise algorithmic bias being introduced.
- Monitor data curation and data quality controls including for synthetic data.
- Identify and select the machine learning or artificial intelligence platform architecture and specific hardware, to contribute to solving a computational problem using allocated resources.
- Identify and embed changes in work to deliver sustainable outcomes.
- Monitor model data drift, using performance metrics to ensure systems are robust when moving outside of their domain of applicability.
- Develop a process to decommission assets in line with policy and procedures. Manage current and legacy models in line with industry approaches.
- Undertake independent, impartial decision-making respecting the opinions and views of others in complex, unpredictable and changing circumstances.
- Coordinate, negotiate with and manage expectations of diverse stakeholders suppliers and multi-disciplinary teams with conflicting priorities, interests and timescales.
- Produce and maintain technical documentation explaining the data product, that meets organisational, technical and non-technical user requirements, retaining critical information.
- Create and disseminate reports, presentations and other documentation that details the model development to confirm stakeholder approval for handover to implementation.
- Comply with equality, diversity, and inclusion policies and procedures in the workplace.
- Horizon scan to identify new technological developments that offer increased performance of data products.
- Apply Machine Learning principles and standards such as, organisational policies, procedures or professional body requirements.
- Integrate AI-based approaches, including those provided by third-party vendors’ Application Programming Interfaces, into existing and new processes.
- Proactive identification of the potential for automation for example through AI solutions embedded within tooling.
Training schedule
You will attend remote weekly workshops and complete self-driven tasks in line with the course content.
More training information
Level 6 Qualification in Machine Learning – Please note: this course is limited to Level 6 content only. It does not include Levels 4 or 5.
Requirements
Essential qualifications
GCSE in:
- English (grade 4 or above)
- Maths (grade 4 or above)
Other in:
Desirable qualifications
Other in:
Share if you have other relevant qualifications and industry experience. The apprenticeship can be adjusted to reflect what you already know.
Skills
- Communication skills
- IT skills
- Organisation skills
- Problem solving skills
- Presentation skills
- Number skills
- Analytical skills
- Logical
- Team working
About this employer
UKRI is an organisation that brings together the seven disciplinary research councils, Research England and Innovate UK. Together, we build an independent organisation with a strong voice and vision ensuring the UK maintains its world-leading position in research and innovation. Supporting some of the world’s most exciting and challenging research projects, we develop and operate some of the most remarkable scientific facilities in the world. We are pushing the frontiers of human knowledge through fundamental research and delivering benefits for UK society and the economy through world-class research, skills and business-led innovation.
Company benefits
30 days annual leave plus Bank Holidays and Christmas Shutdown.
Cycle To Work Scheme.
Employee Discount Scheme.
Disability Confident
A fair proportion of interviews for this apprenticeship will be offered to applicants with a disability or long-term health condition. This includes non-visible disabilities and conditions.
You can choose to be considered for an interview under the Disability Confident scheme. You’ll need to meet the essential requirements to be considered for an interview.
After this apprenticeship
Further career and training opportunities.
Ask a question
The contact for this apprenticeship is:
TECHNICAL PROFESSIONALS LIMITED
The reference code for this apprenticeship is VAC2000006965.
Apply now
Closes in 24 days (Monday 2 February 2026)
After signing in, you’ll apply for this apprenticeship on the company's website.