Arbeitgeber: Mercedes-Benz Group AG

Tätigkeitsbereich: Forschung & Entwicklung incl. Design
Fachabteilung: Functions & Algorithms 3
Gesellschaft: Mercedes-Benz AG
Standort: Sindelfingen
Startdatum: 01.11.2026
Veröffentlichungsdatum: 01.11.2026
Stellennummer: MER00048N9
Arbeitszeit: Vollzeit


Aufgaben

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.


Qualifikationen

- 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 criteriahere .

Severely disabled applicants and applicants with equivalent status are welcome! The representative for severely disabled employees (sbv-sindelfingen@mercedes-benz.com) 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 atmyhrservice@mercedes-benz.comor by phone at 0711/17-99000 (Mon-Fri 10am-12pm & 1pm-3pm).


Benefits


- Barriere­frei­heit
- Kantine, Café
- Kinder­betreuung
- Coaching
- Betriebs­arzt
- Mobilitäts­angebote
- Mit­arbeiter Events
- Mit­arbeiter­rabatte möglich
- Mit­arbeiter­beteili­gung möglich
- Mit­arbeiter­handy möglich
- Essens­zulagen
- Gesund­heits­maß­nahmen
- Hybrides Arbeiten möglich
- Betrieb­liche Alters­ver­sorgung
- Park­platz
- Gute An­bindung
- Flexible Arbeits­zeit möglich


Kontakt

Yutong Yang
Email: yutong.yang@mercedes-benz.com


Kolumbusstr. 19+21 Gebäude 711/0
71063 Sindelfingen