> Markdown version of [/jobs/ext/4121736-working-student-multi-fidelity-test-optimization](https://www.wearedevelopers.com/jobs/ext/4121736-working-student-multi-fidelity-test-optimization). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Working Student Multi-Fidelity Test Optimization - **Company:** BMW AG - **Location:** München, Germany - **Contract:** Temporary contract - **Skills:** Artificial Intelligence, Computer Programming, Python (Programming Language), Tensorflow, Pytorch, Large Language Models, Information Technology - **Published:** October 23, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=c04e16052e9caaf9 ## About the Role * Studies in computer science, mathematics, physics, or a related discipline. * Strong programming skills in Python 3, ideally with experience in 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. * Enthusiasm for developing automated, scalable, and reliable applications for in-car systems. * Motivation to engage in application-oriented research problems. * Business-fluent English; German language skills are a plus. Do you enjoy collaborating with an international team and want to help drive innovation in automotive AI? Then Apply now! ## Description * You will support the development of intelligent, innovative automated evaluation systems for our in-car LLM-based assistant. * Furthermore, you contribute to the design and implementation of test optimization approaches across testing environments, ranging from low-fidelity software execution to high-fidelity in-car testing. * In addition, you help shape our product and process portfolios with your own ideas. * Moreover, you contribute to close collaboration with suppliers and internal teams, exchanging insights and joining technical discussions driving innovation in automotive AI.