The NVIDIA GPU and SoC Architecture group is seeking strong architects with great analytical skills and a deep understanding of system architecture and performance to use your skills creatively on processor and system architecture performance of full applications driving scalable improvements for all of our artificial intelligence/machine learning, automotive, geforce and high-performance computing products. This position offers you the opportunity to have a real impact on the hardware and software that underlies the most exciting trends in modern computing in the world. We are looking for someone who is passionate about and loves what you do and excited about creatively applying what you know to make a difference.
What you'll be doing:
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Performance analysis/ bottleneck analysis of complex, high performance GPUs and System-on-Chips (SoCs).
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Work on hardware models of different levels of extraction, including performance models, RTL test benches and emulators to find performance bottlenecks in the system.
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Work closely with the architecture and design teams to explore architecture trade-offs related to system performance, area, and power consumption.
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Understand key performance usecases for the product. Develop workloads and test suites targeting graphics, machine learning, automotive, video, compute vision applications running on these products.
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Drive methodologies for improving turnaround time, finding representative data-sets and enabling performance analysis early in the product development cycle.
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Develop required infrastructure including performance simulators, testbench components and analysis tools.
What we need to see:
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BE/BTech or MS/MTech, or equivalent experience in relevant area, PhD is a plus.
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2+ years of relevant experience dealing with system level architecture and performance issues.
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Strong understanding of System-on-Chip (SoC) architecture, graphics pipeline, CPU architecture, memory subsystem architecture and Network-on-Chip (NoC)/Interconnect architecture.
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Solid programming (C/C++) and scripting (Bash/Perl/Python) skills. Exposure to Verilog/System Verilog, SystemC/TLM is a strong plus.
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Strong debugging and analysis (including data and statistical analysis) skills, including use of RTL dumps to debug failures.
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Exposure to performance simulators, cycle accurate/approximate models or emulators for pre-silicon performance analysis is a plus.
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Excellent communication and organization skills.
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Ability to work in a global team environment.
NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
Top Skills
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.”