Job Description

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 robotics, agentic modeling and AI model inference across many domains.

• 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:

• Familiar with large model inference frameworks or multi-modality models, knowledge of model inference benchmark.

• Hands-on experience or theoretical knowledge in robotics manipulation, locomotion, simulation, closed-loop validataion and models such as VLA/VAM.

• Familiar with various agent framework is a plus and knowledge of Openclaw, skills.

• Familiar with NVIDIA libraries (e.g., NemoClaw, Isaac, BioNeMo, Dynamo, PhysicsNeMo) as well as published research or open-source contributions in relevant areas.