Shanghai, China Role overview
About this role
Today, NVIDIA is tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. We are the DevTech team serving the robotics ecosystem in China. As NVIDIA DevTechs, we are defined as experts in application optimization on NVIDIA platforms — through code optimization, new application architectures, and cutting-edge DL/ML algorithms. Our mission spans performance analysis & modeling, applying modern DL/ML techniques to real-world problems, deep-diving on GPU architecture, building samples/libraries/SDKs, and collaborating with architecture, software engineering, and product management to improve NVIDIA’s current and future platforms. We are looking for a passionate intern to work on Robotics Foundation Model training and inference — powering the next generation of intelligent robotic systems on NVIDIA’s platform.
What You’ll Be Doing:
Develop and optimize training pipelines for robotics foundation models (e.g., vision-language-action models, world models) on NVIDIA GPU clusters
Profile, analyze, and optimize model inference performance on NVIDIA platforms
Apply modern DL/ML techniques to real-world robotics problems
Collaborate closely with NVIDIA architecture, research, libraries, tools, and system software teams to influence the design of next-generation architectures, software platforms, and programming models
Build samples, reference implementations, and documentation to educate developers on NVIDIA robotics AI technologies
Discuss your approach and results together with senior DevTech engineers
What We Need to See:
Currently pursuing a Master’s or PhD degree in Computer Science, Robotics, AI/ML, Electrical Engineering, or a related technical field
Strong programming skills in Python; familiarity with C/C++ and CUDA is a plus
Experience with deep learning frameworks (PyTorch,)
Understanding of transformer architectures and large-scale model training
Basic knowledge of robotics concepts (kinematics, control, perception, or simulation)
Curious, self-motivated, and excited about solving open-ended challenges at NVIDIA
Ways to Stand Out from the Crowd:
Experience with distributed training (DeepSpeed, Megatron-LM, FSDP, NeMo)
Experience with inference optimization (SGlang, vLLM)
Familiarity with NVIDIA robotics platforms (Isaac Sim, Isaac Lab, GR00T)
Publications or project experience in embodied AI or robotics foundation models
Ambitious to grow and learn about building ML applications, optimization, and software engineering