Machine Learning Engineer Apprentice

Science and Technology Facilities Council (STFC) 
Salary: £24,340 per annum (rising annually throughout the apprenticeship)
Contract Type: Fixed-Term, 24 months 
Hours: Full-time, 37 hours 
Location:  Rutherford Appleton Laboratory, Harwell Campus, Didcot, OX11 0QX
Closing Date: Sunday, 2nd February 2026
Interview Date: Feb 2026 / Mar 2026 
Start Date: September 2026 

 

Are you fascinated by cutting-edge science and emerging technologies? Join us as an Apprentice Machine Learning Engineer and play a hands-on role at the Rutherford Appleton Laboratory Particle Physics department to support world-class research and innovation. This is a unique opportunity to work alongside scientists, engineers, and technologists—contributing to real projects that drive discovery in areas like 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.

 

About an Apprenticeship at STFC

We’re looking for enthusiastic and motivated individuals ready to develop their skills through formal training and hands-on experience. As an apprentice, you’ll gain real-world experience, build technical and professional skills, and contribute to cutting-edge research and innovation in a dynamic, supportive environment.  

You’ll be fully supported throughout the programme with regular check-ins from a dedicated Apprenticeship Coordinator, structured training, practical experience, and mentorship from industry professionals. Plus, you’ll have access to workshops, a peer network, and career development support, helping you feel confident and prepared for new challenges.  

 

Why join the PPD Group at STFC?

The Particle Physics Department (PPD) undertakes and funds world-leading research and development, and data analysis, in particle physics. We are major collaborators on world leading neutrino experiments, dark matter experiment, as well as on the Large Hadron Collider detectors at CERN. We work closely with other departments such as Scientific Computing and Technology.

Machine learning algorithms play a very important role in particle physics experiments as those experiments produce huge amounts of data that has to be analysed to extract the all-important physics. One such experiment is the upcoming Hyper-Kamiokande experiment that is currently being built in Japan. It will be the largest underground neutrino detector in the world, and will start taking data in 2028. This is a great time to join the team to implement machine learning solutions that will benefit the experiment and increase the significance of its findings, potentially having a real impact on cutting edge scientific discoveries. 
 

Qualifications gained  

Machine learning engineer / Skills England

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 and does not lead to a full degree qualification.

 

What you’ll learn: 

You will gain an understanding of the fundamental knowledge that underpins machine learning and AI. You will cover the tools and techniques needed to develop machine learning models as well as the mathematical and statistical theory underpinning those models. You will cover deep neural networks as well as cyber security for machine learning. You will gain an understanding of data engineering principles and the deployment and monitoring of machine learning.

 

Training provider

The qualification will be delivered by Learn Tech: AI, Data & Digital Tech Apprenticeships and Training - LearnTech

You will attend remote weekly workshops and complete self-driven tasks in line with the course content. 

 

What You’ll Be Doing – Day-to-day responsibilities   

• 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.

 

Entry Requirements

The below criteria will be scored during Shortlisting (S), Interview (I) or both (S&I).

We are looking for the following: 

Essential

• GCSEs in Maths and English (Grade 4/C or above) (S) 
• A minimum of two A levels at grade A or above OR three A levels at grade C or above OR a BTEC Level 3 in Computing (or similar subject) or equivalent
  • Please give subjects and resulted/expected results in CV
• An awareness of basic health and safety (I) 
• Right to live and work in the UK at time of starting employment (S)
• Enthusiastic and motivated to learn, both in the workplace and through formal training (I)
• Ability to work in a collaborative team (I)
• Ability to work/learn independently and carry out own research/study to progress with the work (I)
• Ability to examine evidence and data to solve problems (I)
• Ability to visualise multi-dimensional problems and be creative (I)
• The CV and/or cover letter should explain why you have applied for the Machine Learning apprenticeship scheme and should contain examples that involved solving problems and learning, especially in the areas of computing, maths, and/or physics and engineering (S)
• Some knowledge and enthusiasm for computational coding and science (maths, physics) (S)
• Good at keeping things on track and getting tasks done on time (I)

Desirable

• Level 4, 5 or 6 qualifications in a relevant area such as computing, maths, physics, or engineering
  • Please give subjects and results / expected results in CV (S)
• Some understanding of and experience in Machine Learning, either through academic studies or work experience. (S&I) 
• Prior experience having worked on a team project or assignment that involved collaborative work in a STEM/computing area (S&I)
• Good communication skills, both written and spoken (I)
• Ability to create slides (powerpoint, keynote or similar software) to present and evidence progress (S)

 

 Important Information

• To complete the apprenticeship, you will need to evidence passes at 9-4 or A*-C in GCSE (or equivalent) Maths and English. For those sitting their GSCEs in Summer 2026, we would need evidence of your predicted grades.
• When applying for an apprenticeship, there is a set residency eligibility criteria that must be met by the applicant. To check your eligibility please click here.
• The training provider will also ask you to complete an initial assessment during the application process. If you already have a qualification in this subject area or similar please include, as an attachment, your transcript outlining the grade/s achieved and modules covered.  By applying for this apprenticeship, you are giving permission for your details to be shared with the relevant training provider.

We will be in touch after the closing date, however, please reach out on apprenticerecruitment@ukri.org if you have any questions. 

 

Employee Benefits

• Your salary will increase annually as you progress through your apprenticeship, in line with policy.
• 30 days holiday (in addition to 10.5 bank holidays and privilege days).
• Flexible working hours.
• An excellent defined average salary pension scheme.
• Easily accessible public transport links/ free parking.
• Excellent learning and development opportunities.
• Cycle to work scheme.

For a list of our full benefits, please visit here.

 

As this job does not fulfil the UK Government minimum criterion for obtaining sponsored migrant worker status we will be unable to apply for sponsorship for anyone not eligible to work in the UK. At interview, all shortlisted candidates are required to bring with them identification documents and original documents that prove they hold or can obtain the right to work in the UK. You can check your eligibility here: https://www.gov.uk/check-uk-visa/y 

 

How to apply 

Part of our application process involves submitting your CV and a cover letter. Your cover letter should address your suitability for the opportunity based how you meet each essential criteria stated in this advert. 

Please note that we hold the right to close this vacancy early if a sufficient number of applications have been received.  

We ask some of the biggest questions in the universe, to answer some of the biggest challenges in the world. Together, our scientists, technologists, engineers and business support team explore the unknown across every field you could think of. And they turn what they find into work that changes the world around us. 

What could you achieve with the world-leading facilities and experts of one of Europe's largest research organisations by your side? Join us and discover what's possible! 

 

UKRI - UK Research and Innovation

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.

More information can be found at www.ukri.org.

Choosing to come to work at UKRI means that you will have access to a whole host of benefits from a defined benefit pension scheme, excellent holiday entitlement, access to employee shopping/travel discounts and salary sacrifice cycle to work scheme.  For more details, visit benefits of working for UKRI.

How we support EDI in the workforce

At UKRI, we believe that everyone has a right to be treated with dignity and respect, and to be provided with equal opportunities to thrive and succeed in an environment that enables them to do so. We also value diversity of thought and experience within inclusive groups, organisations and the wider community. For further information, please visit ‘How we support EDI in the workforce’.

Disability Confident Employer

As users of the disability confident scheme, any candidate who opts into the scheme and best meets the essential criteria, will be shortlisted for interview. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

How to apply

Online applications only preferred for this role. Please submit a CV and covering letter which clearly outlines how you fulfil the criteria specified along with your motivation for UKRI and the role. Ensure that the job reference number is included in the filename description of each document uploaded. Note that failure to address the above criteria or submit an application without a covering letter may result in the application not being considered. Assessment will only be based upon the content of your submitted covering letter and CV and not the ‘experience’ section of the application.

UKRI seeks to ensure it creates and maintains a system of openness, fairness and inclusion – a collaborative, trusted environment, which is attractive to and accessible to everyone who is interested in developing their career with us.

The role holder will be required to have the appropriate level of security screening/vetting required for the role.  UKRI reserves the right to run or re-run security clearance as required during the course of employment.