2027 AI Researcher Apprentice - Level 6 - Crawley

THALES UK LIMITED
Crawley (RH10 9HA)
Closes on Sunday 7 February 2027
Posted on 5 October 2026

Summary

Working alongside AI researchers, engineers, and domain specialists, you will support research and experimentation on live projects, translating questions into evidence-based findings, prototypes, and recommendations.

Wage £24,000 a year
Minimum wage rates (opens in new tab)
Yearly performance-related pay uplifts

Training course Machine learning engineer (level 6)
Hours Flexible
Working Monday - Thursday, 8 hours per day
Friday, 5 hours
37 hours a week
Start date Monday 6 September 2027
Duration 2 years
Positions available 2

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

• Reviewing technical literature and summarising relevant methods and evidence
• Designing controlled experiments and defining clear evaluation criteria
• Preparing data and developing proof-of-concept machine learning models
• Analysing results, limitations, uncertainty, and potential sources of bias
• Documenting methods and communicating findings through reports, demonstrations, and reviews
• Working with experienced researchers and engineers on live research challenges

Where you'll work
Manor Royal
Crawley
RH10 9HA

Training

Apprenticeships include time away from working for specialist training. You’ll study to gain professional knowledge and skills.

Training provider CORNDEL LIMITED
Training course Machine learning engineer (level 6)
Understanding apprenticeship levels (opens in new tab)

What you'll learn

• 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.