GPU Firmware Infrastructure Engineer

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

NVIDIA is seeking a GPU Firmware Infrastructure Engineer to design and develop tools and infrastructure for GPU firmware. The role involves improving software processes, enhancing build systems, designing solutions for the GPU lifecycle, and inventing AI-powered workflows to boost team output. The position requires strong Python and embedded programming skills, automation experience, and a solid understanding of CI/CD and distributed services. Experience with AI-assisted development tools is a plus.

What you'd actually do

  1. Improve team software process and core infrastructure by enhancing, designing and supporting build systems and regression farms across a variety of platforms
  2. Design, develop, test, debug, and optimize creative solutions for GPU firmware throughout the entire GPU lifecycle
  3. Engage with hardware, software, infrastructure, and business teams to grow new firmware and infrastructure features from idea to reality
  4. Invent and build AI-powered workflows that multiply your own and your team's output
  5. Create, document, and automate workflows, processes, and tooling for teams and their internal-facing and external-facing projects

Skills

Required

  • BS or MS degree in EE/CS/CE (or equivalent experience)
  • 2+ years of relevant software development experience
  • Automation experience with modern CI/CD tools
  • Sturdy technical background in cloud and distributed services
  • Fundamental understanding of database concepts, object modeling, SQL or non-SQL
  • Firm grasp of software development lifecycle, reliability engineering and scalability thinking
  • Strong Python and embedded programming skills
  • Exceptional communication skills: ability to define the right problem, write clear requirements, review critically, and explain trade-offs
  • Strong interpersonal skills

Nice to have

  • Experience in developing device BIOS, firmware, or other low-level embedded software
  • Track record of writing clear technical proposals, design docs, or architecture decisions that others have acted on independently
  • Experience with secure development techniques such as threat models, attack-trees, static/dynamic analysis, fuzzing, and negative testing
  • Experience using AI-assisted development tools: knowing when to trust, when to verify, and when to throw away the output

What the JD emphasized

  • Invent and build AI-powered workflows