Key Tasks:

• Shape the long-term technical direction for AI-native agents, digital workers, and intelligent automation systems.
• Explore frontier AI paradigms and identify how they can become practical product capabilities.
• Design next-generation agent architectures that combine reasoning, memory, self-EVO, knowledge, proactivity, ReAct.
• Explore world model approaches for perception, prediction, simulation, planning, and decision-making.
• Investigate Physical AI opportunities in the automotive industry, including vehicle intelligence, engineering workflows, diagnostics, simulation, and human-machine collaboration.
• Build research-driven prototypes and turn promising ideas into scalable, production-ready systems.
• Develop high-level approaches for agent reliability, controllability, evaluation, observability, and continuous improvement.
• Define the foundation for future AI-native platforms, including agent ecosystems, autonomous workflows, and intelligent interfaces.

Qualification and skills required for the position:

• Master  degree or above in Computer Science, Artificial Intelligence, Robotics, Electrical Engineering, Applied Mathematics, Physics, or a related field. PhD preferred 
• Experience building LLM applications, AI agents, Physical AI, embodied AI, robotics, simulation, planning, reinforcement learning, multimodal models, or world model research is highly preferred.
• Strong foundation in artificial intelligence, machine learning, agent systems, and modern software engineering.
• Ability to understand frontier AI research and judge its practical value for real-world products.
• Systems thinking: able to reason across models, tools, data, workflows, infrastructure, and user experience.
• Strong research taste and technical judgment, with curiosity about where AI-native systems are heading.
• Ability to move between exploration and implementation: read, evaluate, prototype, build, and iterate.