Senior System Architect, GPU

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA +2 · Remote

NVIDIA is seeking a Senior System Architect to define future aspects of their GPU architecture, focusing on AI and accelerated computing. The role involves cross-disciplinary collaboration with software, ASIC design, and other teams to optimize scalability, performance, power efficiency, and area. Responsibilities include developing architecture innovations, benchmarking configurations, enhancing analysis infrastructure, implementing performance models, and documenting specifications.

What you'd actually do

  1. Develop GPU architecture innovations and improvements, optimizing along the axes of scalability/modularity, performance and power efficiency, area, yield, effort, and schedule.
  2. Benchmark GPU configurations (core count, memory and interconnect bandwidth) employing advanced packaging; identify optimal designs for future data center workloads.
  3. Develop and enhance architecture analysis infrastructure, including performance simulators, testbench components and analysis tools, to evaluate configurations under different constraints.
  4. Implement and maintain high-level functional and performance models. Analyze application workloads and performance simulation results to identify areas of architecture improvements.
  5. Document architecture specifications; work with ASIC design, software, and VLSI teams to review and explore trade-offs, define solutions, and track progress.

Skills

Required

  • Master’s/PhD in Computer Engineering, Computer Science or related fields (or equivalent experience)
  • A minimum of 8 years of relevant work experience in GPU or CPU System Architecture development
  • Proficiency in data analysis (Python, Excel) to correlate configuration changes with performance metrics.
  • Deep understanding of accelerated computing and AI data center requirements and tradeoffs, including performance bottlenecks, TCO, Power Delivery Network (PDN), DC Networking, etc
  • Strong communication and interpersonal skills, as well as the ability to thrive in a dynamic, collaborative, distributed team.

Nice to have

  • Experience with GPU architecture, especially in off-chip IO, memory subsystem, and/or Network-on-Chip (NoC)/Interconnect.
  • Knowledgeable in system level functions such as reset and boot, DFT, and power management
  • Expertise in analyzing performance scaling and bottlenecks at device and system levels for AI/accelerated computing workloads
  • Knowledgeable in modern packaging technologies, and their costs and benefits
  • Consistent track record of efficiently implementing complex architectural features
  • Outstanding problem-solving skills with a focus on optimizing performance, area, complexity, and power

What the JD emphasized

  • GPU or CPU System Architecture development
  • Deep understanding of accelerated computing and AI data center requirements and tradeoffs
  • Expertise in analyzing performance scaling and bottlenecks at device and system levels for AI/accelerated computing workloads