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NVIDIA

Designs the chips and software that power AI

ASIC Verification Engineer

Hardware Validation & TestingConfirmed live in the last 24 hours
Full-timeNo salary listed

About the Job

Requirements

  • A Bachelor of Science or Master of Science in Computer Science or Electronics Engineering, or equivalent experience, and 0–2 years of experience.
  • Proficiency in C, C++, and Python.
  • Understanding of system-on-chip and system architecture.
  • Understanding of how system-on-chip devices such as DMA, interrupt engines, CPUs, boot devices, and MMU/IOMMU function.
  • Understanding of operating system and device-driver concepts, and ability to write no-OS test frameworks that run directly on bare-metal system-on-chip hardware.
  • Understanding of hardware and/or software development lifecycles.

Responsibilities

  • Build tools, infrastructure, and test frameworks used in RTL simulations, C simulations, emulation, FPGA, and silicon.
  • Understand Tegra system architecture, with a focus on system-on-chip IP involved in boot and security features.
  • Build tools that enable design and verification teams to access registers, memory, DMA engines, and other unit features, and enable test reuse across platforms including RTL, models, emulation, and silicon.
  • Develop features for silicon bring-up, including FUSE, ATE, security, flash/DMA, board I2C, and USB debug devices.
  • Assess and build C-model or RTL-simulation capability to simulate features before silicon arrival in a pre-silicon environment.

Desired Qualifications

  • Previous experience handling silicon bring-up and enabling emulation/FPGA-related test frameworks.
  • Exposure to testbench architectures, including C++ testbenches and SystemVerilog/UVM-based testbenches.
  • Verilog and SystemVerilog coding skills.

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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