Student for Master's Thesis – AI-based Code Generation & Verification in Automotive Software in MB. OS

Life is always about becoming... In life it is about going on a journey to become the best version of our future self. As we discover new things, we face challenges, master them and grow beyond us.

Apply to Mercedes-Benz and find the area where you can develop your talents individually. You will be supported by visionary colleagues who share your pioneering spirit. Joining us means becoming part of a global team whose goal is to build the most desirable cars in the world. Together for excellence.

Number: MER0003YZC In Group Research & Mercedes Benz Cars Development (RD), we design the automotive generations of the future. This means we mean innovative products with the highest quality and efficient development processes using state-of-the-art technologies. We are already working on vehicles that will secure the technology leadership of Daimler in the future.

In the MB.OS Architecture department, we design the future vehicle generations of Mercedes-Benz Cars with regard to component networking and energy supply across the cross-functional and cross-series.

As a Masterand*in in one of our Energy Supply projects, you work at the interface of artificial intelligence, software engineering and automotive hardware. Their main task is to design and develop an innovative AI-based requirements-to-code-to-hardware workflow that significantly improves the efficiency and quality of the development of safety-relevant automotive software.

Your activities include:

Analysis and Conception: You analyze existing processes for ECU code generation and identify potentials for AI-based automation Building on this, you design a workflow that uses textual requirements (requirements), test cases and manual code reviews as input

AI-based code generation and adaptation: You develop and implement algorithms and models (e.g. based on LLMs and RAG systems) that are able to generate executable ECU code directly from specifications and test cases or to customize existing code based on review feedback

Integration into the development process: You work on seamless integration of your developed workflows into an automated overall process to ensure a continuous toolchain

Quality assurance by AI methods: You explore and implement AI-based methods to improve system development, including: coverage analysis: development of approaches to automatic testing of requirements by test cases Plausibility checks: Conception and implementation of AI models to automatically verify the consistency and plausibility of textual requirements Documentation and evaluation: You document your research results, the developed architecture and the implementation in detail. In addition, evaluate the performance and benefits of your developed system using relevant metrics and applications

The final topics will be discussed with the university, you and us. The activity can now and/or Start March 2026.

Course in Computer Science, Data Science, Artificial Intelligence, Embedded Systems, Mechatronics or Comparable Basics AI/ML: Understanding Machine Learning Algorithms, in particular Large Language Models (LLMs), Natural Language Processing (NLP) and Retrieval-Augmented Generation (RAG) Systems Programming skills: Funded knowledge in Python (ideally 3.10+) are essential, including relevant libraries for AI/ML (e.g. scikit-learn, PyTorch, TensorFlow) and data processing Software Engineering: Understanding Clean Code Principles, Software Architecture and Test Methods (Unit Tests, Integration Tests) Databases: Basic knowledge of vector databases (e.g. FAISS, ChromaDB, Pinecone) and ideally also graph databases (e.g. Neo4j) for knowledge representation Analytical skills: The ability to analyze complex technical specifications and translate them into formal models Research competence: Methodological approach in the design, development and evaluation of new approaches Nice-to-Have: First touch points with automotive standards (e.g. AUTOSAR, ISO 26262) or the concept of embedded systems Experience with Prompt Engineering for LLMs Knowledge of formal methods or verification is a plus

Additional information: We are looking forward to your online application with CV, lettering, certificates, current enrollment certificate with an indication of the semester and proof of the regular study period. Please do not forget to mark your documents as ''relevant for this application' in the online form and to observe the maximum file size of 5 MB. Further information on the setting criteria can be found here. Disabled and equalized applicants are welcome! The severely disabled representative (sbv-sindelfingen@mercedes-benz.com) is happy to support you in the application process. HR Services will be happy to help you with questions about the application process. You can reach us by email via myhrservice@mercedes-benz.com or by phone at 0711/17-99000 (Mo-Fr 10-12am & 13-15am).

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