AI Engineer

datasets, identifying anomalies, drift, and trends to ensure data quality. Design and maintain robust CI/CD pipelines for AI model deployment, ensuring high software engineering standards. Collaborate with aerodynamicists and CFD engineers to translate complex engineering requirements into AI-driven solutions. Stay up to date with the latest academic research and technological advances in AI, CFD, and aerodynamic applications. Identify AI-driven opportunities to improve aerodynamic efficiency, cost-effectively supporting the team's performance goals. Company DescriptionFor almost 50 years, Williams F1 Team has been at the forefront of one of the fastest sports on the planet, being one of the top three most successful teams in history competing in the FIA Formula 1 World Championship. With an almost unrivalled heritage of engineering and racing F1 cars and unforgettable eras that demonstrate it is a force to be reckoned with, the British squad boasts 16 F1 World Championship titles to its

name. Since its foundation in 1977 by the eminent, late Sir Frank Williams and engineering pioneer Sir Patrick Head, the team has won nine Constructors' Championships, in association with Cosworth, Honda and Renault. Its roll call of drivers is legendary, with its seven Drivers' Championship trophies being lifted by true icons of the sport: Alan Jones, Keke Rosberg, Nelson Piquet, Nigel Mansell, Alain Prost, Damon Hill and Jacques Villeneuve. The team has made history before and is out to make it again with a long-term mission to evolve and return to the front of the grid. QualificationsProven experience developing and deploying AI/ML models for scientific or engineering applications. Advanced proficiency in Python (PyTorch) and at least one low-level language (C, C++, Rust, Fortran), including experience maintaining Python bindings. Strong understanding of computational geometry and mesh generation algorithms. Experience with statistical analysis and anomaly detection for large scientific datasets. Solid knowledge of fluid dynamics principles. Experience in motorsport, Formula 1, or aerospace aerodynamics is highly desirable. Excellent communication skills, able to collaborate effectively with aerodynamicists and translate technical insights across domains. Strong software engineering practices, including CI/CD pipelines and code quality maintenance. Ability to manage multiple high-priority projects in a fast-paced, high-pressure environment. Master's or PhD in Engineering, Physics, Computer Science, Mathematics, or related scientific discipline (or equivalent experience). Additional InformationAtlassian Williams F1 Team is an equal opportunity employer that values diversity and inclusion. We are happy to discuss reasonable job adjustments.#J-18808-Ljbffr Similar jobs, Senior Safety Engineer/ Safety Engineering Manager - Autonomous & AI Systems Qualifications, skills, and all relevant experience needed for this role can be found in the full description below. Location: London (Hybrid - 3 days in office) Employment Type: Permanent, Full...

Job Description Senior AI Engineer London - 1-2x week in office Salary: £80,000 - £90,000 This is an exciting opportunity to join a business that is investing heavily in AI and modern cloud technologies. You will play a key role in building AI-driven products that have...

ResponsibilitiesWilliams F1 Team is seeking a Senior Aerodynamic AI Engineer to join our Aerodynamics department, working at the cutting edge of data, artificial intelligence, and vehicle performance. This role will focus on transforming aerodynamic data from CFD simulations, wind tunnel measurements, track telemetry, and geometric information into actionable insights using advanced AI and machine learning techniques. This is an exciting opportunity to combine aerodynamic engineering expertise with AI innovation, directly contributing to the performance of a Formula 1 car in a high-pressure, fast-paced environment. Develop and deploy AI/ML models for aerodynamic data analysis, including CFD simulations, wind tunnel, and track data. Lead the creation of advanced surrogate models for aerodynamic predictions, particularly in fluid dynamics applications. Integrate theoretical, experimental, and real-world data to enhance aerodynamic insights beyond CFD. Analyse wind tunnel and track