Intern Autonomous Driving

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Job-ID: MER00048N9 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 ( 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 at myhrservice@mercedes-benz.com or by phone at 0711/17-99000 (Mon-Fri 10am-12pm & 1pm-3pm).

Benefits

Meal-Discounts

Mobile Phone for Employees Possible

Discounts for Employees Possible

Annual Profit Share Possible

Events for Employees

Coaching

Flextime Possible

Hybrid Work Possible

Health Benefits

Company Retirement

Mobility Offers

Parking

Inhouse Doctor

Good Public Transport

Barrier-Free Workplace

Near-Site Childcare

Canteen, Café

Contact

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

Mercedes-Benz Plant Sindelfingen, Sindelfingen Kolumbusstr. 19+21 Gebäude 711/0 71063 Sindelfingen