Deep Learning Performance Architect Intern




Multiple Locations


Position summary

cuSparse and cuTensor libraries

  • Design and develop software for shipping and testing the GPU operators

  • Build scalable automation for testing, integration, and release processes for publicly distributed deep learning libraries

  • Configure, maintain, and build upon deployments of industry-standard tools (e.g., Kubernetes, Jenkins, Docker, CMake, Gitlab, Jira, etc)

What we need to see:

  • Pursuing a B.S., M.S., or PhD degree in computer science (or similar)

  • Strong programming skills in C/C++ development

  • Familiar with GPU programming model and CUDA

  • Good understanding about AI compilation technologies and experience with MLIR, TVM development

  • Excellent problem solving skills, good communication and teamwork

NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative and autonomous, we want to hear from you!