NVIDIAPosted 1d ago

Performance Software Intern, Deep Learning Libraries - 2027

Performance Software Intern, Deep Learning Libraries - 2027 at NVIDIA scores 90 out of 100 on AI centrality, which makes it a Level 4 role on this board.

China, ShanghaiinternFull time

AI in this role

Develop optimized code and compute kernels to accelerate linear algebra and deep learning operations on NVIDIA GPUs.

tensorflowcudacutlasopenmppthreads

deep-learninggpu-optimizationparallel-programmingsoftware-engineering

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

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Deep LearningGpu OptimizationParallel ProgrammingSoftware EngineeringTensorFlowCudaCutlasOpenmp

Questions you could be asked

Tell me about a project where deep learning was part of your work. What did you do?
Tell me about a project where gpu optimization was part of your work. What did you do?
Tell me about a project where parallel programming was part of your work. What did you do?
Tell me about a project where software engineering was part of your work. What did you do?
Walk me through how you've used TensorFlow in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Deep Learning, Gpu Optimization, Parallel Programming, Software Engineering, and TensorFlow. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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