NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by phenomenal technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. Doing what’s never been done before takes vision, innovation, and the world’s best talent. We are now looking for Design Automation Tool development Engineers for our Tegra SOC development group. As the Lead, you will be responsible for architecting and crafting end-end tool flow to auto-generate system fabrics, sub-systems and SOC hierarchies, keeping user interface, maintainability and scalability in mind.
One should possess Strong software development skills and a dedication to high quality work. A proven track record to conceptualize, design and implement modular and robust EDA tools with well thought out APIs and interfaces would make you an excellent fit. Having a deep understanding of object oriented programming is a prerequisite for this role. If you have a zeal to make things better, we will have an excellent match for our needs.
What you’ll be doing:
Define next gen EDA workflows for the world's top chip designers and verification engineers.
Architect and develop tools, methodologies that bring AI in chip design cycle.
Analyze productivity bottlenecks, improve data analytics and visualizations of day-to-day workflows
Debug interesting, complex, and important problems that impact the team
What we need to see:
BS/MS in Computer Science or Computer Engineering or Electrical Engineering or equivalent experience
2+ years of relevant work experience
Strong problem-solving abilities and a passion for debugging
Software architecture, development, & testing experience
Solid understanding of Linux and excellent coding skills (Perl/Python)
Excellent communication and interpersonal skills
Ways to stand out from the crowd:
Exposure to RTL design/verification tools (VCS or equivalent simulation tools, debug tools like Verdi) and methodologies (UVM or equivalent)
Experience implementing scalable infrastructure solutions with frontend UX, RESTful APIs, database, visualization, and backend automation.
Experience in deploying ML components in various stages of tool.
Linux administration experience, revision control and CI/CD systems such as Jenkins
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What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
