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Senior Performance Software Engineer, Deep Learning Libraries

NVIDIA· Architect
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Shanghai, Beijingfull-timePosted 4w ago

About the role

Senior performance software engineer focused on developing optimized deep learning library code to accelerate linear algebra and deep learning operations on NVIDIA GPUs, contributing to cuDNN, cuBLAS, and TensorRT and collaborating across CUDA compiler, DL performance teams, and hardware/architecture groups.

Responsibilities

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

Qualifications

  • Masters or PhD degree or equivalent experience in Computer Science, Computer Engineering, Applied Math, or related field
  • 2+ years of relevant industry experience
  • Demonstrated strong C++ 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

Skills

Required:C++ programmingPerformance-oriented parallel programmingComputer architecture
Last updated Sep 25, 2026. Interested? Apply on the company site.

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NVIDIA invents the GPU and drives advances in AI, HPC, gaming, creative design, autonomous vehicles, and robotics.

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