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NVIDIA

Designs the chips and software that power AI

Architect – Performance Verification and Analysis

Hardware Validation & TestingConfirmed live in the last 24 hours
Full-timeBengaluru, Karnataka, IndiaNo salary listed

About the Job

Requirements

  • BE/BTech or MS/MTech, or equivalent experience in relevant area, PhD is a plus
  • 2+ years of relevant experience dealing with system level architecture and performance issues
  • Strong understanding of System-on-Chip (SoC) architecture, graphics pipeline, CPU architecture, memory subsystem architecture and Network-on-Chip (NoC)/Interconnect architecture
  • Solid programming (C/C++) and scripting (Bash/Perl/Python) skills. Exposure to Verilog/System Verilog, SystemC/TLM is a strong plus
  • Strong debugging and analysis (including data and statistical analysis) skills, including use of RTL dumps to debug failures
  • Exposure to performance simulators, cycle accurate/approximate models or emulators for pre-silicon performance analysis is a plus
  • Excellent communication and organization skills
  • Ability to work in a global team environment

Responsibilities

  • Performance analysis/ bottleneck analysis of complex, high performance GPUs and System-on-Chips (SoCs)
  • Work on hardware models of different levels of extraction, including performance models, RTL test benches and emulators to find performance bottlenecks in the system
  • Work closely with the architecture and design teams to explore architecture trade-offs related to system performance, area, and power consumption
  • 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
  • Drive methodologies for improving turnaround time, finding representative data-sets and enabling performance analysis early in the product development cycle
  • Develop required infrastructure including performance simulators, testbench components and analysis tools

Desired Qualifications

  • Strong background in System Level Performance aspects for Graphics and High Performance Computing
  • Exposure to GPU application programming interfaces like CUDA, OpenGL, DirectX
  • Expertise in data analysis and visualization

About the Company

NVIDIA

Designs the chips and software that power AI

NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1993

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