Aero AI Industrial Placement: ML for F1 Performance

Cadillac Formula 1 Team; Indianapolis (IN); On-site; USD 30,000 - 50,000; Full time

Job summary

Cadillac Formula 1 Team in Indianapolis is seeking a Machine Learning / AI Industrial Placement student to join our multidisciplinary data team and work on cutting-edge AI tools that support performance development. You will apply ML to analyze technical imagery, simulations, and high‑dimensional engineering data, collaborating with engineers and domain experts.

Responsibilities include developing models, embeddings, and retrieval systems, applying LLMs to summarize technical information, and

Qualifications

• Currently pursuing a degree in CS/Engineering/Data Science or related field.
• Strong Python programming skills and ML fundamentals.

Responsibilities

• Develop ML models to analyze technical imagery, simulations, and engineering data.
• Identify trends, differences, and anomalies in complex datasets for performance analysis.
• Create embedding models and similarity metrics for intelligent data comparison.
• Apply LLMs to summarize and synthesize technical information for engineers.
• Design retrieval-augmented systems using historical engineering and simulation data.
• Build internal tools and interfaces to help engineers extract insights from data.
• Deploy and test prototype systems locally using modern frameworks.
• Collaborate with domain experts to guide model development and interpret outputs.

Skills

Python

ML fundamentals

Computer vision / NLP / IR

Clean, documented code

Independent yet collaborative

Education

Degree in CS/Engineering/Data Science

Tools

PyTorch

Git

Docker

Linux

Job description

Cadillac Formula 1 Team in Indianapolis is seeking a Machine Learning / AI Industrial Placement student to join our multidisciplinary data team and work on cutting-edge AI tools that support performance development. You will apply ML to analyze technical imagery, simulations, and high‑dimensional engineering data, collaborating with engineers and domain experts.

Responsibilities include developing models, embeddings, and retrieval systems, applying LLMs to summarize technical information, and