AI4Science Solution Architecture Intern - 2026 job opportunity at NVIDIA.

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DatePosted 4 Days Ago bot
NVIDIA AI4Science Solution Architecture Intern - 2026
Experience: General
Pattern: full-time
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loacation China, Beijing, China
loacation China, Beijing....China

We're seeking outstanding interns to participate in our AI and accelerated computing projects. As an AI4Science Solution Architecture Intern, you’ll collaborate with world-class experts, contribute to groundbreaking innovations, and help build the future of artificial intelligence and high-performance computing. This is an outstanding opportunity to gain hands-on experience while working on real-world projects that make a significant impact! What you'll be doing: Use your skills in programming, AI, and accelerated computing to build innovative tools and applications in areas such as AI for Science (AI4S), robotics, and computational modeling. Conduct AI engineering work, assist in developing and optimizing AI models and tools using NVIDIA SDKs and frameworks. Collaborating with internal teams and external researchers. Explore brand new trends in AI and computing acceleration to contribute to research and technology transfer projects. Be available 3–4 days per week for at least 6 months. Positions are primarily based in Beijing, Shanghai, or Shenzhen. What we need to see: Enrolled in a Master’s or Ph.D. program in Computer Science, Electrical Engineering, Applied Mathematics, or a related field. Solid programming experience in at least one language (Python, C/C++, etc.) and familiarity with Linux development environments. Strong analytical and problem-solving skills. Effective communication and a collaborative approach when working with multi-functional teams. Ways to stand out from the crowd: Hands-on experience or theoretical knowledge in accelerated computing, machine learning, deep learning, or AI4S fields. Familiar with large model inference frameworks or multi-modality models, knowledge of model inference benchmark. Familiarity with modern AI models such as transformers or diffusion models, and understanding of optimization methods. Experience with CUDA programming and popular deep learning frameworks (PyTorch, TensorFlow, etc.). Familiar with NVIDIA libraries (e.g., Modulus, Isaac, BioNeMo, CUDA-Q, PhysicsNeMo) as well as published research or open-source contributions in relevant areas.

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