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NVIDIA

Designs the chips and software that power AI

DPU Performance Validation Engineer

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

About the Job

Requirements

  • A B.Sc. in Electrical Engineering, Computer Engineering, or equivalent is required.
  • At least 3 years of experience in ASIC development or validation is required.
  • A strong background in ASIC debugging is required, including reading RTL, analyzing waveforms, and determining root causes of functional or performance issues.
  • Hands-on experience with performance validation and analysis at the system level, involving die-level or multi-die systems, is required.
  • Proficiency in Python and C/C++ in a Linux environment is required.

Responsibilities

  • Learn and analyze the system-level operation of NVIDIA DPUs.
  • Debug and determine the root causes of performance issues in pre-silicon environments, using RTL, waveform traces, and multi-die system simulations.
  • Collaborate with design, verification, architecture, and performance modeling teams to isolate and fix issues.
  • Develop and improve validation methodologies for performance experiments and data collection.
  • Automate repetitive debugging and validation tasks to scale coverage and efficiency.
  • Validate performance and functional behavior of current and future NVIDIA silicon devices across single-die and multi-die systems.

Desired Qualifications

  • Expertise in performance modeling, traffic generation, or architecture studies is desirable.
  • Experience with modern interconnects and protocols such as PCIe, Ethernet, or CHI is desirable.
  • Familiarity with emulation platforms such as Palladium, Veloce, or FPGA prototyping is desirable.

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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