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What you'll be doing:
As a member of our deep learning architecture team, you will contribute to features that help next-generation GPUs advance the state of AI.
This position requires you to keep up with the latest DL research and collaborate with diverse teams (internal and external to NVIDIA), including DL researchers, hardware architects, and software engineers.
Your day to day work will include analyzing the behavior of various deep learning methods, proposing new features to accelerate or enable various methods, and studying the benefits of the proposed features.
What we need to see:
MS or PhD degree in computer science, computer architecture, electrical engineering or related field or equivalent experience.
5+ years of relevant experience in at least a few of the following relevant areas is required in your work history:
Computer architecture;
Performance analysis and optimization;
Experience with deep learning workloads, including performance tuning considerations such as parallelization and fusion strategies;
Experience with core deep learning kernels such as matrix multiply and convolution
Programming fluency with C++ and ideally Python
Experience with GPU computing (CUDA)
Experience with deep learning frameworks like PyTorch
You will also be eligible for equity and .
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