Level 6 Machine Learning (AI) Engineering Apprenticeship
Network Rail Limited
York (YO1 6JT)
Closes in 11 days (Monday 15 June 2026)
Posted on 3 June 2026
Contents
Summary
On our Level 6 Machine Learning Engineer Apprenticeship, you’ll help shape how Artificial Intelligence is used across the railway. Delivered in partnership with GBRX, Network Rail and leading organisations across the rail industry, build the skills needed to apply AI in real-world environments.
- Wage
-
£24,500 a year
- Training course
- Machine learning engineer (level 6)
- Hours
-
Monday to Friday
Shifts to be confirmed
35 hours a week
- Start date
-
Monday 14 September 2026
- Duration
-
2 years
- Positions available
-
8
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
Over 22 months you’ll develop your skills through a combination of structured learning and hands-on experience — applying what you learn to real industry challenges.
As part of the programme, you’ll build and develop machine learning models using real-world data, support the deployment of AI solutions into live environments, and work on projects linked to innovation across the rail industry. You’ll collaborate with data, engineering and technology teams, while monitoring and improving model performance to ensure your work delivers real impact.
You’ll also gain exposure across organisations including GBRX, Network Rail and wider industry partners, helping you build a broad understanding of how AI is applied across the railway.
Where you'll work
George Stephenson House
Toft Green
York
YO1 6JT
Training
Apprenticeships include time away from working for specialist training. You’ll study to gain professional knowledge and skills.
Training provider
NETWORK RAIL INFRASTRUCTURE 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
Requirements
Essential qualifications
GCSE in:
- English (grade C/4)
- Maths (grade C/4)
A Level in:
Desirable qualifications
A Level 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
- Attention to detail
- Organisation skills
- Problem solving skills
- Number skills
- Analytical skills
- Initiative
About this employer
We own, operate, maintain and develop the railway infrastructure in England, Scotland and Wales.
That’s 20,000 miles of track, 30,000 bridges, tunnels and viaducts and the thousands of signals, level crossings and stations. We manage 20 of the country’s largest stations. The rest – over 2,500 – are run by the train operating companies.
https://www.earlycareers.networkrail.co.uk/ (opens in new tab)
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
You’ll gain the experience and skills to move into roles such as:
- Machine Learning Engineer
- AI Engineer
- Data Engineer
- Machine Learning Operations Engineer
Ask a question
The contact for this apprenticeship is:
NETWORK RAIL INFRASTRUCTURE LIMITED
The reference code for this apprenticeship is VAC2000034441.
Apply now
Closes in 11 days (Monday 15 June 2026)
After signing in, you’ll apply for this apprenticeship on the company's website.