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NVIDIA

System Software Engineer, GPU Development Tools

NVIDIA
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mid full-time Shanghai

First indexed 18 May 2026

Description

A key part of NVIDIA's strength is our sophisticated development tools and modelling environments that enable our incredible pace of delivering new technology to market.

We are looking for forward-thinking, hard-working, and creative people to join a multifaceted software team with high production-quality standards. This software engineering role involves developing high-level chip models, test APIs and trace generation workflows, and analysis tools. As a member of the software development team, you will engineer and improve the core infrastructure for execution, automation, and debugging the development of large-scale, general-purpose graphics and computing chips. This infrastructure enables our driver stack, applications, tests, and studies to run unchanged on all functional, diagnostic, and performance models.

Responsibilities:

  • Play a critical part in every stage of development of a GPU.
  • Improve the daily workflows of the world's top chip modelers and designers to help produce the next greatest generation of GPUs.
  • Empower GPU architects to understand application performance today and model competition-destroying performance for tomorrow.
  • Coordinate with architecture and software teams to enable functional and performance testing for the next architecture.

Requirements:

  • Bachelor's or higher degree in Computer Science, Computer Engineering, or related major.
  • 2+ years of experience.
  • Aptitude to work across the GPU, driver, and application stacks.
  • Strong C/C++ is a must-have capability.
  • Fluent in written and spoken English.
  • Python programming experience (a plus).
  • AI related knowledge (a plus).

Nice to Have:

  • Know-how working on operating system kernels or writing device drivers with strong systems-level debugging skills.
  • A knowledge of GPU APIs such as DirectX, CUDA, Vulkan or OpenGL.
  • Experience with chip and/or system simulation.
  • Deep understanding of systems architecture: CPU, GPU, memory, display, buses, kernel internals would be helpful.
  • Advanced programming expertise with full-stack web-based visualization technologies to help provide data insights.