Senior ASIC Front End Infrastructure Engineer

NVIDIA•Durham, NC

32d•$184,000 - $356,500

About The Position

NVIDIA is seeking elite ASIC Infrastructure engineers to deliver the tooling and environment that enables DV and RTL Design for the world's leading GPUs. This position offers the opportunity to have a real impact on a dynamic, technology-focused company impacting product lines ranging from consumer graphics to artificial intelligence, 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, and others have given up? Rapidly learning and deploying best in class internal and industry solutions including AI harnesses in secure environments that you then use to build Skills and Agents for Infrastructure Automation? 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 using AI tools for development and automation
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

Deploy AI toolsets at scale in secure configurations for use by all HW Design teams
Use ML/DL/AI techniques to automate Infrastructure work and improve Design team productivity
Improve the speed, flexibility and extensibility of the GPU front end build flow
Keep the GPU Continuous Integration system at the cutting edge of source management methodologies
Guide compute farm, filer, and network topology requirements at cloud scale
Deploy and continuously improve compute farm technologies such as containers, volume cloning, distributed storage and distributed compute at cloud scale
Forecast HW Design compute resource and EDA needs, with reporting up to the CFO’s office
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!