Aerodynamic AI Engineer

MSMagazin; Grove; On-site; GBP 50,000 - 80,000; Full time

Job summary

MSMagazin is seeking an Aerodynamic AI Engineer to leverage data and AI for improving aerodynamic development at Williams Racing. This role involves developing machine learning models and collaborating with engineers in a fast-paced environment.

The ideal candidate excels in Python, has experience with CI/CD pipelines, and an advanced degree in engineering or a related field. Opportunities for growth and innovation in aerodynamics await.

Qualifications

• Experience developing/deploying AI/ML models for scientific applications.
• Proficiency in Python and PyTorch framework.
• Familiarity with mesh generation algorithms.
• Experience with statistical analysis and anomaly detection.
• Strong software engineering practices including CI/CD development.

Responsibilities

• Develop AI models for CFD simulation data analysis.
• Build surrogate models for aerodynamic predictions.
• Create AI-driven mesh generation algorithms.
• Analyze wind tunnel data for quality assurance.
• Maintain CI/CD pipelines for AI model deployment.
• Collaborate with engineers to translate requirements into AI solutions.

Skills

AI/ML model development

Python

Computational geometry

Statistical analysis

CI/CD pipeline development

Communication skills

Project management

Fluid dynamics

Education

Master's or PhD in Engineering, Physics, Computer Science, or related

Tools

PyTorch

NVIDIA PhysicsNemo

Job description

The Aerodynamic AI Engineer is part of a dedicated team leveraging data and artificial intelligence to enhance aerodynamic development, performance analysis, and operational efficiency within the Aerodynamics department. Reporting to the Lead AI Engineer, you will work on specialist projects that bridge aerodynamic engineering and AI — developing advanced machine learning models, surrogate models, and automated geometry tools that give Williams Racing a competitive edge in vehicle development.

This is a technically demanding, hands‑on role in a fast‑paced, high‑pressure environment. You will collaborate closely with aerodynamicists and CFD engineers, translating complex engineering requirements into practical AI solutions and communicating your findings with clarity and impact.

What you’ll do

• Develop and deploy AI models for the analysis of CFD simulation data, extracting insights to support aerodynamic development decisions.
• Build advanced surrogate models for aerodynamic predictions, with a particular focus on fluid dynamics applications.
• Develop AI-driven mesh generation algorithms and automated geometry creation tools for aerodynamic applications.
• Conduct comprehensive analysis of wind tunnel data, including drift detection, anomaly identification, and statistical analysis to ensure data quality and reliability.
• Build and maintain robust CI/CD pipelines for AI model deployment, ensuring high code quality standards across all aerodynamic AI applications.
• Collaborate with aerodynamicists and CFD engineers to translate engineering requirements into AI solutions and communicate complex insights effectively.
• Stay current with emerging AI technologies relevant to computational fluid dynamics and aerodynamic applications.
• Identify AI-driven opportunities to improve aerodynamic development efficiency within cost cap requirements.

Qualifications

• Proven experience developing and deploying AI/ML models, particularly for scientific or engineering applications.
• Strong proficiency in Python with the PyTorch framework.
• Understanding of computational geometry principles and familiarity with mesh generation algorithms.
• Experience with statistical analysis and anomaly detection techniques for large scientific datasets.
• Strong software engineering practices including CI/CD pipeline development and code quality standards.
• Excellent communication skills, with the ability to collaborate across technical disciplines and translate complex AI concepts for engineering audiences.
• Demonstrated ability to manage multiple projects and deliver results in a fast‑paced, high‑pressure environment.
• Master's or PhD in Engineering, Physics, Computer Science, Mathematics, or a related scientific discipline (or equivalent practical experience).
• Experience with NVIDIA PhysicsNemo or similar physics-informed machine learning frameworks.
• Knowledge of fluid dynamics concepts and CFD data analysis.
• Experience in motorsport, Formula 1, or aerospace aerodynamics.