Senior AI Application Developer - GPU and Soc Architecture Modeling

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA +1 · Remote

Senior AI Application Developer role focused on developing and deploying scalable GenAI applications to accelerate GPU/SOC architecture modeling. The role involves integrating LLMs into existing workflows, collaborating with hardware architects and infrastructure engineers, and researching emerging AI technologies. Requires proficiency in C++, Python, ML frameworks, and hands-on experience with LLMs and multimodal models.

What you'd actually do

  1. Develop and deploy scalable GenAI applications that integrate with existing workflows and enhance overall productivity
  2. Use state-of-the-art AI tools and techniques to accelerate GPU/ SOC Architecture modeling
  3. Work with hardware architects to identify how to best design, customize, and deploy AI-based solutions to their specific problem domains.
  4. Collaborate with infrastructure engineers to improve existing automated workflows by incorporating LLMs and establishing best practices for future solutions.
  5. Research emerging AI technologies and engineering best practices to continuously evolve our development ecosystem and maintain a competitive edge.

Skills

Required

  • BS, MS, PhD or equivalent experience in Computer Science, Electrical Engineering, Computer Engineering, or a related field with 3+ years of experience in related areas
  • Proficiency in C++, Python and ML frameworks like LangChain, LangSmith, Claude Agent SDK, Nemo Agent Toolkit, or other AI agentic tools
  • Hands-on experience with LLMs (e.g. GPT, Claude, Llama) and multimodal models
  • Strong collaboration skills with design and engineering teams
  • Strong problem-solving and debugging skills

Nice to have

  • Background in Computer Architecture with experience in modeling is a plus

What the JD emphasized

  • track record of driving issues to closure

Other signals

  • Develop and deploy scalable GenAI applications
  • Use state-of-the-art AI tools and techniques to accelerate GPU/ SOC Architecture modeling
  • Collaborate with infrastructure engineers to improve existing automated workflows by incorporating LLMs