Autobrains, a global leader in AI-based vision technologies for the automotive industry, is expanding its core leadership team.

We are looking for a visionary Chief System Architect to lead the design and evolution of real-time AI infrastructures and cutting-edge vision-based systems at the edge.

This is a key executive role, reporting directly to the CEO, with broad ownership over system architecture and strategic technology development.

• Define and lead the architecture of Autobrains' next-generation AI and real-time systems for edge deployment.
• Build scalable, robust, and efficient infrastructures to support deep learning, vision, and perception models in real-world environments.
• Oversee the integration of real-time computing frameworks, AI inference engines, and system-level services across heterogeneous hardware platforms.
• Lead technical alignment across AI research, algorithm, hardware, and software teams to ensure seamless system design and execution.
• Establish system-wide best practices for real-time performance, resource management, inter-process communication (IPC), scheduling, and fault tolerance.
• Guide the selection and integration of industry-standard and proprietary real-time technologies (e.g., RTOS, ROS 2, Adaptive Autosar).
• Evaluate and integrate new hardware accelerators (GPU, NPU, DSP) to optimize AI execution pipelines under strict real-time constraints.
• Stay at the forefront of advancements in real-time AI, embedded vision technologies, and system design methodologies, and incorporate them into the company's technical strategy.

• Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related field.
• 10+ years of experience in system architecture, real-time systems, and AI/vision-based infrastructure.
• Proven leadership experience driving complex system architecture in innovative technology companies.
• Deep technical expertise in real-time operating systems (e.g., QNX, Linux RT, Zephyr) and real-time scheduling.
• Strong background in designing AI runtime systems, including deployment of deep learning models on edge devices.
• Proficiency in C/C++, system-level Python, and hardware-software optimization techniques.
• Hands-on experience with edge computing architectures involving CPUs, GPUs, NPUs, and custom accelerators.
• Familiarity with automotive-grade system design, including safety standards like ISO26262 – an advantage.
• Excellent communication, collaboration, and leadership skills, with a hands-on and strategic mindset.