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Working Student Autonomous Driving Research

Mercedes-Benz Group AG · Sindelfingen, Germania · Onsite

  • Ufficio in Sindelfingen
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Are you passionate about robots taking over the world? Then look no further! We are constantly looking for highly motivated students to join our autonomous car research team in Stuttgart, Germany in the following area:

Navigating urban areas presents unique challenges for autonomous vehicles, requiring sophisticated algorithms and custom-generated data to ensure safety and reliability. Within the publicly funded projects STADT:up (stadtup-online.de) and nxtAIM (nxtaim.de), we are dedicated to developing advanced algorithms to address these challenges. Our research focuses on developing novel neural network architectures that enhance the safety and reliability of autonomous vehicles in urban areas. In addition to that, we are pioneering methods to generate new data, which is vital for training and optimizing our models. These innovative approaches aim to improve the performance and adaptability of autonomous driving systems, enabling them to navigate complex urban scenarios safely and efficiently.

You can look forward to working with a diverse team of international colleagues and enjoying good coffee as well as the occasional cake (we love it when you bring one to share!).

We are conducting research on two main domains:

  • Adopting Generative Models for Autonomous Driving:
    • As part of the nxtAIM project, this initiative focuses on using generative AI to create new data crucial for training and optimizing autonomous vehicle models, enhancing their adaptability and performance in complex urban environments
  • Improving 3D Object Detection for End-to-End Autonomous Driving:
    • Under the STADT:up project, this effort aims to refine 3D object detection algorithms, crucial for perception systems. You'll enhance the accuracy and reliability of detecting and interpreting objects in real-time for safer urban navigation

In our department, you'll have the opportunity to explore the development of cutting-edge autonomous driving algorithms. Furthermore, you can gain valuable insights into how these advanced systems are integrated into our vehicles, enhancing safety and driving experience.

The tasks of the working student will include:

  • Helping with data handling
  • Conduct literature research to familiarize yourself with the latest advancements in autonomous driving and generative models
  • Implement generative and 3D object detection models using frameworks such as PyTorch and deploy them on cloud infrastructure
  • Analyze and evaluate algorithm performance using publicly available datasets, identifying strengths and areas for improvement
  • Participate in regular meetings to discuss project progress, share insights, and support collaborative problem-solving

Working students can start working from August 2025.

We are conducting research on two main domains:

  • Adopting Generative Models for Autonomous Driving: 
    • As part of the nxtAIM project, this initiative focuses on using generative AI to create new data crucial for training and optimizing autonomous vehicle models, enhancing their adaptability and performance in complex urban environments
  • Improving 3D Object Detection for End-to-End Autonomous Driving: 
    • Under the STADT:up project, this effort aims to refine 3D object detection algorithms, crucial for perception systems. You'll enhance the accuracy and reliability of detecting and interpreting objects in real-time for safer urban navigation

In our department, you'll have the opportunity to explore the development of cutting-edge autonomous driving algorithms. Furthermore, you can gain valuable insights into how these advanced systems are integrated into our vehicles, enhancing safety and driving experience.

The tasks of the working student will include:

  • Helping with data handling
  • Conduct literature research to familiarize yourself with the latest advancements in autonomous driving and generative models
  • Implement generative and 3D object detection models using frameworks such as PyTorch and deploy them on cloud infrastructure
  • Analyze and evaluate algorithm performance using publicly available datasets, identifying strengths and areas for improvement
  • Participate in regular meetings to discuss project progress, share insights, and support collaborative problem-solving

Working students can start working from August 2025.


  • Pursuing a master’s degree in a highly quantitative field such as Computer Science, Physics, Mathematics, Electrical Engineering, or adjacent fields
  • Fluency in English and/or German
  • Strong programming skills in an object-oriented programming language, particularly Python
  • Familiarity with Linux
  • Ability to think proactively and work independently
  • Experience with deep learning frameworks such as PyTorch
  • Familiarity with Computer Vision, Foundation Models, Generative AI
  • Understanding of Convolutional Neural Networks/Vision Transformers/Diffusion Networks

Additional information:

  • The position is based at our research lab in Sindelfingen
  • Ideally candidates are available for a duration of 6-12 months
  • For further information, please feel free to contact us via email

It doesn’t work completely without formalities. When sending your online application, please attach your CV, certificate of enrollment, current performance record, relevant certificates, if applicable proof of mandatory internship and the standard period of study (max. 5 MB) and mark your application documents as "relevant for this application" in the online form.

Please find the criteria of employment "here".

Citizens of countries outside the European Trade Union please send, if applicable, your residence / work permit.

We particularly welcome online applications from candidates with disabilities or similar impairments in direct response to this job advertisement. If you have any questions, you can contact the local disability officer once you have submitted your application form, who will gladly assist you in the onward application process: [email protected]

Please understand that we no longer accept paper applications and that there is no right to get your documents returned.

If you have any questions regarding the application process, please contact HR Services by e-mail at [email protected] or by phone: 0711/17-99000 (Monday to Friday between 10 a.m. to 12 a.m. and 1 p.m. to 3 p.m.).