Senior GPU Architect, Performance and Scalability

NVIDIA•Us, CA

22h

About The Position

This is an outstanding opportunity to join a world-class team and play a pivotal role in crafting the future of GPU technology. At NVIDIA, you will work with dedicated individuals in an inclusive and collaborative environment where your hardworking nature will drive flawless execution and ambitious projects.

Requirements

Bachelors/Master’s/PhD in Computer Engineering, Computer Science or related fields (or equivalent experience)
A minimum of 10 years of relevant work experience in GPU or CPU System Architecture development
Proficient programming skills in C++ and Python.
Solid background in Computer Architecture with experience in modeling
Strong communication and interpersonal skills, as well as the ability to thrive in a dynamic, collaborative, distributed team.
Strong problem-solving and debugging skills, with a track record of driving issues to closure

Nice To Haves

Experience with GPU architecture and/or Network-on-Chip (NoC)/Interconnect.
Knowledgeable in system level functions such as reset and boot, DFT.
Consistent track record of efficiently implementing complex architectural features
Outstanding problem-solving skills with a focus on optimizing performance, area, complexity, and power.

Responsibilities

Develop GPU architecture innovations and improvements, optimizing along the axes of scalability/modularity, performance, area, yield, effort, and schedule.
Develop and enhance chip design infrastructure, including functional models and yield simulators, testbench components and analysis tools, to evaluate configurations under different constraints.
Develop tests, test plans and testing infrastructure for new architectures/ features
Work in a matrixed environment, across the different modeling teams, to document, design, develop tools to optimize chip yield and performance
Implement and maintain functional, performance, and yield models
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 choices against performance, cost, and scalability targets.
Guide the improvement of functional verification tools and expand other resources to support future GPU architectures