Nvidia Senior System Architect GPU 

United States, Texas 

499760928

What you will be doing:

• Develop GPU architecture innovations and improvements, optimizing along the axes ofscalability/modularity,performance and power efficiency, area, yield, effort, and schedule.

• Benchmark GPU configurations (core count, memory and interconnect bandwidth) employing advanced packaging; identify optimal designs for future data center workloads.

• Develop and enhance architecture analysis infrastructure, including performance simulators, testbench components and analysis tools, to evaluate configurations under different constraints.

• Implement and maintain high-level functional and performance models. Analyze application workloads and performance simulation results to identify areas of architecture improvements.

• Document architecture specifications; work with ASIC design, software, and VLSI teams to review and explore trade-offs, define solutions, and track progress.

• Collaborate with other functional teams (Design, Floorplan, Packaging and Systems Engineering, etc) to validate packaging choices against performance, cost, and scalability targets.

What we need to see:

• 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.

Ways to stand out from the crowd:

• 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