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Silicon Software Engineer - System and AI (RDSS Intern)

NVIDIA Corporation

Taiwan, Taipei, Taiwan, HsinchuKernel / LinuxFirmwarePosted Sep 16, 2026
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Skills named in this posting

  • Python
  • Device drivers
By submitting your resume, you’re expressing interest in our 2027 RDSS ( Research and Development Substitute Services) program. Please confirm your eligibility with the local district office before applying for the role. NVIDIA has continuously reinvented itself over two decades. NVIDIA’s invention of the GPU in 1999 fueled the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Now, we are now looking for System Software Engineer - Silicon (RDSS Intern)! You will work directly in the CPU software fields across various products (Server, Embedded, and Automotive) What you’ll be doing: • Develop and debug system software across pre-silicon simulation/emulation and physical hardware environments, including simulator/emulator/platform bring-up, chip-level firmware development, integration, and validation. • Apply modern GenAI techniques and tools, from prompt engineering to agentic, graph-based workflow orchestration, to debug, improve, and optimize existing engineering workflows such as evidence retrieval, debug triage, and root-cause analysis. • Validate AI-generated results against authoritative engineering evidence, ensuring that engineers retain ownership of final technical decisions. • Collaborate with senior engineers and global teams, and produce high-quality technical documentation. What we need to see: • MS in CS, CE, EE, Systems Engineering or related software/hardware engineering major. • Ability to independently break down ambiguous and complex engineering problems into manageable, testable tasks. • Strong programming skills in C, with working knowledge of Python or another scripting language. • Solid understanding of embedded systems, computer architecture, and ARM-based platforms. • Practical familiarity with modern GenAI concepts and tools, along with the ability to evaluate AI-generated results critically against engineering evidence. • Written and verbal communication skills in English, with the ability to collaborate effectively across global teams. Ways to stand out from the crowd: • Strong system-level debugging and problem-solving skills, including the ability to analyze logs, traces, failures, and complex hardware-software interactions. • Experience with pre-silicon simulation or emulation, platform bring-up, chip firmware, device drivers, or post-silicon debugging. • Experience building debug tools, test infrastructure, workflow automation, APIs, or developer productivity solutions. • Understanding of GenAI evaluation, evidence grounding, data provenance, observability, and failure analysis. Experience using AI-assisted tools to improve a real engineering workflow, with measurable improvements in quality, efficiency, or debugging time. • Curiosity, sound technical judgment, and a strong motivation to learn and grow through practical contributions. We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

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Original posting: jobs.nvidia.com

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RISC-V ecosystem

NVIDIA Corporation in the RISC-V ecosystem

RISC-V International: Premier Board

Founded in 1993, NVIDIA is an AI computing company. The company is headquartered in Santa Clara, California.

  • NVIDIA is bringing CUDA platform support to RISC-V, i.e. allowing a RISC-V CPU to host CUDA GPU applications
  • Publicly credited the RVA23 profile as the enabler: "We wouldn't have considered porting CUDA to RISC-V without it"
  • Active in RISC-V International profile/standards work and a frequent presence in RISC-V ecosystem coverage

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