OpenNash Jobs
Mercedes-Benz Group

Intern Autonomous Driving 239967 Mer00048N9

Early career

Listed on Mercedes-Benz Group’s official careers site. Listed as of October 20, 2026. Check that it is still open and apply there.

Apply on Mercedes-Benz Group’s careers site →

Job description

Tasks In our department for Autonomous Driving Research and Development, we are committed to shaping the future of mobility through the development of highly automated driving systems for highway and urban environments. Our work covers the research, development and validation of advanced machine learning methods, perception systems and decision-making algorithms for next-generation autonomous vehicles. For this exciting and challenging area, we are looking for an Intern Autonomous Driving (Mandatory Internship) to support our research activities in uncertainty estimation for end-to-end autonomous driving.

These challenges await you

Conducting a comprehensive literature review of state-of-the-art end-to-end autonomous driving approaches and uncertainty estimation methods Evaluating and selecting suitable uncertainty estimation techniques for integration into end-to-end autonomous driving systems Integrating and validating uncertainty estimation methods within the end-to-end driving pipeline Analyzing the impact of uncertainty on planning and decision-making, particularly in novel and rare driving scenarios

Implementing, optimizing and evaluating machine learning frameworks for training and validation purposes What you can expect: Insights into cutting-edge research and development in autonomous driving Collaboration with experienced experts in machine learning and automated driving technologies Opportunities to contribute to innovative research projects with real-world relevance Access to state-of-the-art development and evaluation environments An international and interdisciplinary working environment The activity can begin from November 2026.

Qualifications

Studies in the field of Computer Science, Robotics, Physics, Mathematics, Electrical Engineering or a comparable course

  • Strong programming proficiency in Python
  • Solid understanding of deep learning methods, particularly neural networks, as well as experience with common software frameworks such as PyTorch and the MMDetection family Hands-on experience with Linux and software development in Linux environments Very good communication skills and proficiency in English
  • Ability to work in a team Analytical way of thinking and strategic way of working Engagement Preferred qualifications: Knowledge of perception, prediction, planning and uncertainty estimation Experience publishing research results at deep learning or robotics conferences, including collaborative publications Hands-on experience with containerization technologies such as Docker
  • Familiarity with Large Language Models (LLMs), Vision Language Models (VLMs) and Vision-Language-Action Models (VLAMs)

Additional Information

We look forward to receiving your online application, including a resume, cover letter, certificates, current certificate of enrollment stating your semester, proof of mandatory internship if applicable, and proof of the standard period of study. Please remember to mark your documents as "relevant for this application" in the online form and observe the maximum file size of 5 MB. You can find further information on the hiring criteria here .

Severely disabled applicants and applicants with equivalent status are welcome! The representative for severely disabled employees ( [email protected] ) will gladly support you in the application process. People Solutions will be happy to help you with any questions you may have about the application process. You can reach us by email at [email protected] or by phone at 0711/17-99000 (Mon-Fri 10am-12pm & 1pm-3pm).

More jobs at Mercedes-Benz Group

Similar jobs at other companies

OpenNash lists jobs from employers’ own careers sites and does not handle applications. Dates come from the employer where stated; “first seen” and “last checked” are our own observations.