Aerodynamics Industrial Placements

Closing Date: Friday 2nd October

Fueled by bold ambition

We are offering two exciting Industrial Placement opportunities within our Aerodynamics Department, giving students the opportunity to contribute to the development of a Formula 1® car at the highest level of motorsport.

Whether your interests lie in experimental testing and wind tunnel operations, or artificial intelligence and machine learning, you'll be working alongside experienced engineers on real-world projects that directly support aerodynamic performance development.

As part of our team, you'll gain hands-on experience, develop valuable technical skills, and contribute to innovative engineering solutions in a fast-paced, high-performance environment.

1) Aerodynamics Test Engineering

Working alongside experienced engineers, you will support the development and operation of wind tunnel experimental testing and contribute to projects aimed at improving the quality, efficiency and repeatability of aerodynamic testing.

This is an opportunity to gain hands-on experience within a high-performance engineering environment, developing your understanding of aerodynamics, experimental methods, instrumentation, data analysis and engineering software.

You will have the opportunity to work on real engineering challenges and contribute to the development of new approaches, technologies and tools that support experimental operations.

• Support engineers in the planning, preparation and execution of wind tunnel experimental tests
• Assist with the development and improvement of wind tunnel testing methodologies, processes and procedures
• Analyse and interpret experimental data to assess test results and identify opportunities for improvement
• Support the operation, development and evaluation of aerodynamic measurement systems, instrumentation and sensors
• Assist with the investigation and development of new experimental techniques and technologies
• Develop engineering tools and applications to support test planning, data processing, analysis and operational activities
• Contribute to the automation and improvement of repetitive experimental processes
• Document testing activities, methodologies, analysis and results clearly and effectively

2) Aerodynamics AI & Machine Learning

We have an exciting opportunity for a Machine Learning / AI Industrial Placement student to join our Aerodynamics Department and work at the forefront of performance-driven engineering.

Formula 1® generates vast quantities of aerodynamic, simulation and engineering data. Converting this information into actionable insight is critical to performance development. Working within a multidisciplinary engineering and data team, you will help develop next-generation AI tools that support real engineering decision-making.

This placement offers a unique opportunity to apply cutting-edge machine learning techniques within a Formula 1® environment and contribute to tools used by engineers across the department.

• Develop machine learning models to analyse technical imagery, simulation outputs and high-dimensional engineering data
• Identify trends, differences and anomalies within complex datasets to support performance analysis. Create embedding models and similarity metrics to enable intelligent comparison of engineering data
• Apply large language models (LLMs) to summarise and synthesise technical information
• Design retrieval-augmented systems using historical engineering and simulation data
• Build internal tools and interfaces that help engineers extract insight from data
• Deploy and test prototype systems using modern software frameworks
• Work closely with engineers and domain experts to guide model development and interpret outputs

Requirements

What do we need from you?

Aerodynamics Test Engineering

• Currently studying towards a degree in Mechanical Engineering, Aeronautical Engineering, or a related engineering discipline
• Strong interest in aerodynamics, experimental engineering and engineering technology
• Good understanding of engineering principles, mathematics and data analysis
• Analytical and methodical approach to problem solving
• Strong communication and teamwork skills

Aerodynamics AI & Machine Learning

• Currently studying Computer Science, Engineering, Data Science, Mathematics, Artificial Intelligence, or a related discipline
• Strong Python programming skills
• Good understanding of machine learning fundamentals
• Experience in at least one of the following areas:

• Computer Vision and image-based machine learning

• Natural Language Processing (NLP), Large Language Models (LLMs), or information retrieval systems
• Ability to write clean, structured and well-documented code
• Curious, proactive and able to work independently while collaborating effectively with others

A team like no other.

The Cadillac Formula 1® Team challenges conventions and redefines success through bold ambition, cutting-edge innovation, and an unwavering commitment to precision and excellence-on and off the track. This includes offering industry-leading pension, generous time off and, as part of a global brand, huge potential for career development.

As an equal opportunities employer, we are committed to the equal treatment of all current and prospective employees and does not condone discrimination on the basis of age, disability, sex, sexual orientation, pregnancy or maternity, race or ethnicity, religion or belief, gender identity or marriage and civil partnership. We aspire to have a diverse and inclusive workplace and strongly encourage suitably qualified applicants from a wide range of backgrounds to apply.

At Cadillac Formula 1 ®Team, all Team Members are expected to actively support and uphold our policies and procedures, including those focused on Environmental responsibility, Sustainability initiatives, Inclusion and Health and Safety practices.

Please note that additional security checks may be required as part of the recruitment process. This may include a background check covering a minimum of the past five years and a criminal record check.