Senior GPU Architect, Deep Learning

NVIDIA · Semiconductors · Santa Clara, CA +2

NVIDIA is seeking a Senior GPU Architect to design and enhance GPU architecture features specifically for deep learning workloads, covering both training and inference. The role involves developing simulators, mapping deep learning algorithms to hardware, and advancing parallel computation. Requires strong C++, C++, Perl, Python programming, and a background in computer architecture and high-performance computing.

What you'd actually do

  1. Design new hardware features for future processing architectures targeted at deep learning workloads, for both training and inference.
  2. Advance the state of parallel computation.
  3. Be knowledgeable about future parallel programming models and their impact to hardware.
  4. Develop software for various hardware simulators, test infrastructures or metrics systems including databases.
  5. Work in a team to document, design, develop tools to analyze and simulate, validate, and verify functional or performance models.

Skills

Required

  • MS in Computer Science, Electrical Engineering or Computer Engineering or equivalent experience
  • Experience in working with hardware targeted at deep learning, or working on mapping deep learning algorithms to hardware
  • 8+ years of relevant industry experience in GPU or other parallel programming architectures
  • Strong programming ability in C, C++, Perl and Python
  • Background in computer architecture, parallel processing, signal processing and/or high performance computing

Nice to have

  • Knowledge of state of the art in DL algorithms and attention mechanisms

What the JD emphasized

  • deep learning workloads
  • training and inference
  • parallel programming models
  • hardware simulators
  • deep learning algorithms
  • parallel processing architectures

Other signals

  • GPU architecture for deep learning
  • training and inference performance
  • parallel programming models
  • hardware simulators
  • new architectural features