12-Month Aerodynamics AI Industrial Placement

Closing Date: Friday 30th October 2026

We have an exciting opportunity for a Machine Learning / AI Industrial Placement student to join a Formula 1® team working at the forefront of performance-driven engineering. Formula 1® generates vast quantities of technical, simulation, and operational data each week, and transforming this data into actionable insight is critical to on-track performance.

As part of a multidisciplinary engineering and data team, you will work closely with engineers and domain experts to develop next-generation AI tools that support performance development. This placement offers a unique opportunity to apply cutting-edge machine learning techniques directly within a Formula 1® environment and contribute to tools used in real engineering decision-making.

• Developing machine learning models to analyse technical imagery, simulation outputs, and high-dimensional engineering data
• Identifying trends, differences, and anomalies within complex datasets to support performance analysis
• Creating embedding models and similarity metrics to enable intelligent comparison of engineering data
• Applying large language models (LLMs) to summarise and synthesise technical information for engineering users
• Designing retrieval-augmented systems using historical engineering and simulation data
• Building internal tools and interfaces to support engineers in extracting insight from data
• Deploying and testing prototype systems locally using modern software frameworks
• Working closely with domain experts to guide model development and interpret outputs

Requirements

What do we need from you?

• Currently studying for a degree in a relevant subject such as Computer Science, Engineering, Data Science, or a related discipline
• Strong Python programming skills
• Solid understanding of machine learning fundamentals
• Experience in at least one of the following areas:
• Computer vision or image-based machine learning
• Natural language processing, LLMs, or information retrieval systems
• Ability to write clean, well-structured, and well-documented code
• Proactive mindset, curiosity, and the ability to work independently while collaborating with others

Desirable Skills

• Experience with deep learning frameworks (PyTorch preferred)
• Exposure to multi-modal models combining vision and language
• Familiarity with embedding techniques and vector databases
• Interest in engineering, simulation, or data-driven performance analysis
• Experience with scientific Python libraries (NumPy, SciPy, Pandas)
• Familiarity with Git version control, Docker, and Linux-based environments

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.