As an intern at NIO, you will collaborate with various teams to develop AI tools and applications, engage in AI research, and enhance web applications while leveraging skills in full-stack development and web technologies.

The Sr. AI Infra Development Engineer will architect technical solutions for AIOS capabilities across ECU domains, lead proof-of-concept developments, and partner with engineering teams to support AIOS implementations. They will define an evolution roadmap and deliver requirements for development teams while monitoring industry trends.

The Sr. Staff System Architect will lead technical research in software safety and stability for vehicle projects. Their responsibilities include designing software platform safety architecture, performing functional safety analysis, managing stability and reliability of RT-Linux/Microkernel systems, and enhancing software safety requirements.

The Sr/Staff AI Infrastructure Engineer will architect solutions for AIOS, lead proof-of-concept developments, partner with engineering teams, and define the evolution roadmap for AIOS. Responsibilities include collaborating on product development and integration while delivering requirements for the development team and staying updated on industry trends.

Design and implement a microkernel-based OS for autonomous driving applications. Develop low latency network core stacks and system tool chains. Optimize OS functions for performance and security while mentoring team members during the process.

The Sr. Staff GPU Virtualization Software Engineer will architect and develop high-performance GPU virtualization solutions for vehicles, focusing on technical advancements in display and graphics with QEMU. Responsibilities include partnering with engineering teams, optimizing software performance, and delivering GPU virtualization software on time with high quality.

The Sr AI Infra Development Engineer will architect solutions for the AIOS foundation, develop proof-of-concepts, collaborate on AI designs for product development, and define the evolution roadmap for AIOS solutions. They require expertise in AI model training, cloud infrastructure, and software development.

The role involves designing and implementing a microkernel-based OS for autonomous driving vehicles, optimizing OS and network core functions, and mentoring team members. Responsibilities also include developing low-level system code on ArmV8 platforms and contributing to fast diagnostics and troubleshooting tools.

Design and implement a microkernel-based operating system and hypervisor for autonomous driving vehicles. Optimize OS functions for performance, security, and safety, while mentoring team members and collaborating on design implementations.

The intern will research and optimize Large Language Models for deployment in automotive technology, collaborating with teams to integrate these models into real-world applications, focusing on improving their inference efficiency on diverse hardware systems.

The Sr. AI/LLM Infrastructure SW Engineer will architect solutions for AIOS, lead proof-of-concept developments, collaborate with engineering teams for high-quality implementations, and define the evolution roadmap for AIOS solutions. This role requires extensive experience in software design, specifically for AI models, and proficiency in programming languages such as C, C++, and Python.

As an LLM Algorithmic Optimization Engineer Intern, you will research and apply technologies to optimize Large Language Models (LLMs) for efficient inference and deployment across diverse hardware. The role involves collaboration with teams to integrate optimized models into automotive applications and covers the entire pipeline from research to deployment on GPUs and other systems.

The LLM Algorithmic Optimization Engineer will conduct research and implement optimization techniques for Large Language Models across heterogeneous architectures, ensuring efficient deployment in automotive applications. Responsibilities include model optimization, collaboration with cross-functional teams, and contributions from research to deployment using advanced hardware. A strong focus on GPU/NPU architecture is required, along with proficiency in AI tools.