AI Architecture

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.