Dimitris Panagiotopoulos

Formula 1 Software Interview Prep

Note: This course is an add-on to the Automotive and Motorsport Software Specialisation course.

Gain the foundational knowledge required to land your dream job in Formula 1 software. This course and interview prep extends on the essential concepts in automotive software development and testing with techniques and tools used exclusively in Formula 1. Alongside this, there are specific Interview Prep materials from the author’s and his connections’ experiences in Formula 1 software. Learn the essential concepts of Motorsport-specific ECU application design, track data analysis, software testing in F1 and much more.

Everything needed to set yourself apart from other applicants.

Provisional Contents

This course provisionally covers the following topics (as much as I am allowed to share!):

  • Formula 1 Specific Software and Tools
  • Formula 1 Interview Prep (Technical and Behavioural)
  • Software Testing in F1
  • ECU Communication Protocols Interview Prep
  • Battery Management Systems Interview Prep
  • Embedded Automotive Software Interview Prep
  • Model-Based Software Development Practice Exercises and Tests

Course Name

Formula 1 Software Interview Prep

Course Duration

10 hours (approximately)

Delivery

Online Learn at your own pace

Course Level

Intermediate (see prerequisites)

Certificate

Included

Aimed For

Automotive software enthusiasts who want to extend their knowledge and gain industry-specific insights to land their dream entry-level role in Formula 1.

About the Author

Dimitris is a former Software and Controls Engineer at Mercedes AMG HPP and current Software Engineer at Breathe Battery Technologies. He has worked in the F1 and Advanced Technologies divisions at Mercedes and with industry-leading automotive clients at Breathe. He is passionate about entrepreneurship and engineering-related software.

Dimitris has designed and implemented several features that are now present on the 2023 Mercedes F1 car. Most notably, he developed a novel energy management feature, taking it from an algorithm prototype to full deployment on the Mercedes F1 car.