The place to find available career opportunities at NVIDIA for you and people you know. We are now looking for a Performance Software Intern for Deep Learning Libraries! Do you enjoy tuning parallel algorithms and analyzing their performance? If so, we want to hear from you! As a deep learning library performance software intern, you will be developing optimized code to accelerate linear algebra and deep learning operations on NVIDIA GPUs. Join the team that is building the underlying software used across the world to power the revolution in artificial intelligence! We’re always striving for peak GPU efficiency on current and future-generation GPUs. To get a sense of the code we write, check out our CUTLASS open-source project showcasing performant matrix multiply on NVIDIA’s Tensor Cores with CUDA. This specific position primarily deals with code lower in the deep learning software stack, right down to the GPU HW.
 

What you'll be doing:

• Writing highly tuned compute kernels to perform core deep learning operations (e.g. matrix multiplies, MoE, Attention)
• Following general software engineering best practices including support for regression testing and CI/CD flows
• Collaborating with teams across NVIDIA:

Compiler team on generating optimal assembly code
Deep learning training and inference performance teams on which layers require optimization
Hardware and architecture teams on the programming model for new deep learning hardware features
 

What we need to see:

• Pursuing Masters or PhD degree in Computer Science, Computer Engineering, Applied Math, or related field
• Demonstrated strong programming and software design skills, including debugging, performance analysis, and test design
• Experience with performance-oriented parallel programming, even if it’s not on GPUs (e.g. with OpenMP or pthreads)
• Solid understanding of computer architecture and some experience with assembly programming
• Identify bottlenecks, optimize resource utilization, and improve throughput.

Ways to stand out from the crowd:

• Tuning deep learning library kernel code
• CUDA GPU programming
• Numerical methods and linear algebra
• LLVM, TVM tensor expressions, or TensorFlow MLIR