2 months ago
Shanghai, ChinaIntern
Responsibilities
- Analyze and optimize performance on current and next-generation NVIDIA GPUs.
- Analyze the interaction between hardware and software architectures, algorithms, programming models, and applications.
- Collaborate with hardware design, software engineering, product, and research teams on accelerated-computing direction.
- Investigate accelerated-computing applications to support software-hardware co-design.
- Document and present work through white papers, conference publications, blog posts, and patent applications.
Requirements
- Pursuing a B.Sc., M.Sc., or Ph.D. in a relevant discipline such as computer science, electrical engineering, or computer engineering.
- Hands-on experience with massively parallel GPU programming using CUDA or OpenCL.
- Solid background in GPU and computer systems architecture.
- Strong knowledge of C and C++, software design, programming techniques, and algorithms.
- Familiarity with MPI, OpenSHMEM/NVSHMEM, and Python is preferred.
- Strong communication, organization, problem-solving, time-management, and task-prioritization skills.
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.