Senior AI Systems Engineer

About

INNOVATION IS IMAGINING WHAT NO ONE ELSE CAN.

Artificial Intelligence and machine learning are at the cutting edge when it comes to shaping next-gen mobility in areas like automated driving. But it takes real intellectual leadership and expertise to identify and then harness the AI trends and technologies that will transform the way people travel for good.

From system strategy to working prototype, this role is about designing the AI architecture for the AI-defined vehicle of tomorrow. Defining how LLMs, VLAMs, End2End driving models, Foundation world models, and agent systems integrate into ADAS architectures and making sure those decisions hold up as the system scales. Your work will directly influence the onboard AI-design of the complete future ADAS system form L2+ human like driving to L4 autonomous driving.

What awaits you?

• You define the AI system strategy for ADAS: model architectures, data flows, integration concepts: Grounded in methodological expertise and validated through technical feasibility assessments.
• Furthermore, you stay close to the code: prototypes, evaluation pipelines, and data models take shape in your hands and are consistently tested against real-world product requirements.
• You will work hand-in-hand with expert teams, applying your deep AI knowledge to co-create cutting-edge ADAS products spanning L2 to L4 autonomous driving.
• Additionally, you shape how ADAS systems interact with other AI systems within the vehicle, including future voice-based vehicle interactions.
• Also you work in an international, interdisciplinary team directly at the Autonomous Driving Campus Munich, alongside partners from the high-tech industry.

What should you bring along?

• University degree in Computer Science, Data Science, AI, Mathematics, Physics, or comparable qualifications.
• At least five years of experience developing state-of-the-art AI methods with a proven ability to translate these into production-ready system designs.
• You are both architect and practitioner: you can conceive an architectural decision and technically validate it the next day.
• Experience in complex AI or embedded systems, ideally in automotive, robotics, or other safety-critical environments.
• Strong teamwork and communication skills, as well as a proactive and independent work style to drive tasks forward autonomously.