Senior Asic Infrastructure Engineer

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

Senior ASIC Infrastructure Engineer at NVIDIA, focusing on defining and deploying AI/ML applications to enhance chip design, debug, and verification processes. The role involves collaborating with hardware design teams, architecting AI/LLM solutions, and staying updated on AI/ML advancements.

What you'd actually do

  1. Define and deploy AI and ML applications to aid chip design, debug & verification stages.
  2. Collaborate closely with our hardware chip design teams to understand their infrastructure needs and map them onto AI/LLM solutions.
  3. Architect and develop novel tools, user interfaces, and creative applications.
  4. Stay on top of latest advancements in AI/ML technologies and frameworks (LLM memories, transformers, RL, RAG, GenAI) and advocate for their integration within the company.
  5. Troubleshoot and resolve critical technical or productivity obstacles that impact team’s performance.

Skills

Required

  • BS/MS in Computer Science or Computer Engineering or Electrical Engineering or equivalent experience
  • 5+ years of relevant work experience
  • Strong software architecture, development, & testing experience.
  • Solid understanding of Linux, proficient in programming skills (ex. Python / Perl) and version control systems (P4 / git).
  • Prior exposure to RAGs (Retrieval-Augmented Generation) or MCP or LangGraph based agentic AI workflows
  • Strong problem-solving abilities and a passion for debugging
  • Excellent communication and interpersonal skills

Nice to have

  • Exposure to RTL design/verification tools (VCS or equivalent simulation tools, debug tools like Verdi) and computer architecture.
  • Experience in creating scalable AI/LLM based applications for a wide user base.
  • Experience with complete CI/CD workflow (ex. Jenkins, k8s, LSF, Docker, gitlab), interfacing with REST APIs, webservers.
  • A strong passion for continual learning and keeping abreast of new technologies and approaches in AI/ML infrastructure.

What the JD emphasized

  • Prior exposure to RAGs (Retrieval-Augmented Generation) or MCP or LangGraph based agentic AI workflows
  • Strong software architecture, development, & testing experience.
  • Solid understanding of Linux, proficient in programming skills (ex. Python / Perl) and version control systems (P4 / git).

Other signals

  • Define and deploy AI and ML applications to aid chip design, debug & verification stages.
  • Collaborate closely with our hardware chip design teams to understand their infrastructure needs and map them onto AI/LLM solutions.
  • Architect and develop novel tools, user interfaces, and creative applications.
  • Stay on top of latest advancements in AI/ML technologies and frameworks (LLM memories, transformers, RL, RAG, GenAI) and advocate for their integration within the company.