Senior ASIC Front End Infrastructure Engineer

NVIDIA•Austin, CA

65d•Hybrid

About The Position

NVIDIA is seeking elite ASIC RTL/Verification ASIC engineers to develop the core Verification and RTL infrastructure of the world's leading GPUs. This position offers the opportunity to have a real impact in a dynamic, technology-focused company impacting product lines ranging from consumer graphics to artificial intelligence to self-driving cars and supercomputers. Our team of dedicated Infrastructure engineers continuously upgrades the NVIDIA Hardware design environment. We focus relentlessly on Infrastructure improvement so that HW designers can focus relentlessly on their jobs, with Infrastructure that “just works” at worldwide scale and peak performance. Do you like root causing a flow failure, even if it’s rare and hard to reproduce? Learning and deploying best in class internal and industry solutions? Instrumenting flows to measure performance and then improving efficiency? Can you do that at scale, seamlessly transitioning teams from old flows? Then join us!

Requirements

Masters Degree in Electrical Engineering, Computer Engineering, Computer Science or related or equivalent experience
8+ years of relevant work experience
Programming proficiency in Python, Perl, or other Systems Programming language. OO design preferred.
Experience with Make based build systems in large, distributed computing environments
Continuous Integration pipeline and/or pre-submit verification flow experience, for example using Jenkins
You should display a tenacity to root cause and fix Infrastructure problems, especially intermittent, hard to isolate issues in a complex computing environment
Verification domain knowledge with complex ASICs or CPUs using techniques such as random stimulus, functional coverage and assertion-based verification methodologies
Strong problem-solving, debugging and analytical skills
Good interpersonal skills and ability & desire to work as a great teammate

Responsibilities

Define and deploy ML/AI techniques to improve Infrastructure efficiency and support
Improve the speed, flexibility and extensibility of the GPU front end build flow
Keep the GPU Continuous Integration system at the cutting edge of industry techniques
Guide compute farm, filer, and network topology requirements
Deploy new compute farm technologies such as containers, volume cloning, distributed storage and distributed compute at scale
Deploy tracking metrics to resolve operational issues, drive forecasting, and improve design productivity
Deploy information security methods for HW design
Remove inefficiency wherever you can find it!