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求职招聘

NVIDIA 深度学习性能分析架构师

发表至求职招聘

深度学习性能分析架构师 - 上海,新竹


工作职责:

  • 针对架构和算法特征开发汇编级或者CUDA代码
  • 优化cuBlas、cuDNN、TensorRT的核心kernel;
  • 针对未来GPU架构开发原型代码,推进下一代架构的设计和优化;

基本要求:

  • 严谨的逻辑思维和分析能力
  • 较强编程能力(C/C++)、算法分析和实现
  • 熟悉计算机体系结构 优先
  • 有CUDA代码调优经验(或者SIMD等架构的调优经验) 优先
  • 熟悉矩阵计算的优化和加速优先

简历投递:sbai@nvidia.com

详细JD如下


Deep Learning Performance Architect - Shanghai/Hsinchu

Are you obsessed with performance? Do you like to work at the intersection of hardware and software? Do you live and breathe deep learning? NVIDIA is seeking world class programmers and performance architects who love to squeeze out every cycle of performance from deep learning codes. In this role, you will write code that ships in our deep learning libraries, as well as guide the direction of our future GPU architectures. This position offers the opportunity to have real impact in a fast-moving, technology-focused company.

What you'll be doing:

  • Develop state of the art, performance critical code to accelerate deep learning on NVIDIA's platforms.
  • Develop innovative HW, DSP, GPU and system architectures to extend the state of the art in deep learning performance and efficiency
  • Analyze and prototype key deep learning and data analytics algorithms and applications
  • Understand and analyze the interplay of hardware and software architectures on future algorithms and applications
  • Collaborate across the company to guide the direction of machine learning, working with software, research and product teams

What we need to see:

  • MS or PhD in relevant discipline (CS, EE, Math)
  • Track record of optimizing code for performance on CPUs or GPUs, including assembly or SIMD programming
  • Strong mathematical foundation in machine learning and deep learning
  • Experience working with deep learning frameworks like Caffe, TensorFlow and Torch
  • Strong programming skills in C, C++, Perl, or Python
  • Familiarity with GPU computing (CUDA, OpenCL, OpenACC) and HPC (MPI, OpenMP)
  • Strong background in computer architecture
  • Experience with matrix multiply and
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