Master Thesis Multi-Fidelity Test Optimization

Our team at the BMW Group turns advances in artificial intelligence into real in-car experiences. We support the development of innovative, automated evaluation systems for our in-car LLM-based Assistant. Together, we explore multi-fidelity test optimization approaches that connect software-driven and in-car testing environments.

Please note: Submit your complete application documents for review. This position requires on-site presence, and your thesis must be supervised by a university.

Would you like to support the development of intelligent automotive AI testing solutions within an international team? Then Apply now!

• You support the development of intelligent, automated evaluation systems for our in-car LLM-based Assistant.
• Furthermore, you contribute to the design of test optimization approaches ranging from software-driven text-based testing to high-fidelity in-car testing.
• In addition, you help ensure your ideas become an integral part of our product and process portfolios.
• Moreover, you contribute to close collaboration with suppliers and internal teams, exchanging insights and supporting technical discussions that drive automotive AI innovation.

• Studies in computer science, mathematics, physics, or a related discipline.
• Strong programming skills in Python 3 and ideally experience with machine learning frameworks such as TensorFlow or PyTorch.
• Hands-on experience or strong interest in evaluating AI systems, including designing evaluation frameworks and performance measurement.
• Interest in applying optimization approaches to real-life problems.
• Passion for developing automated, scalable and reliable applications for in-car systems.
• Motivation to contribute to application-oriented research problems.
• Business-fluent English; German language skills are a plus.