Applied AI Engineer

NVIDIA NVIDIA · Semiconductors · CA +3 · Remote

NVIDIA is seeking an Applied AI Engineer to develop and integrate AI solutions into their chip design and automation infrastructure. The role involves architecting and implementing AI systems to enhance efficiency and scalability, focusing on LLM-powered validation pipelines, cross-team AI integration, technology scouting, and impact measurement. Requires strong Python, experience deploying ML/AI systems, and familiarity with silicon development environments.

What you'd actually do

  1. LLM-Powered Validation Pipelines: Design and deploy AI systems that make post-silicon validation faster, smarter, and more scalable across semiconductor environments. You're not maintaining what exists, you're building what comes next.
  2. Cross-Team AI Integration: Work directly with multi-functional engineering teams across the organization to identify where AI can eliminate friction, and then build the solution. Your output will be felt across teams, products, and generations of silicon.
  3. Technology Scouting & Evaluation: Evaluate emerging AI frameworks and architectures before the rest of the industry catches on. Be the person who spots what's worth adopting, and makes the case for it.
  4. Impact Measurement & Continuous Improvement: Build the data systems that prove what's working. Establish clear, quantitative indicators of AI impact, close performance gaps, and drive iteration across the org to turn insight into lasting improvement!

Skills

Required

  • BS, MS, or PhD or equivalent experience in CS, EE, CE, or a related field
  • 5+ years of hands-on experience building and deploying ML/AI systems or data-intensive backend services
  • 2+ years of direct Applied AI experience independently owning an AI agent, LLM-powered workflow, or intelligent automation system end-to-end — from prototype through production deployment
  • Strong Python skills
  • proficiency in at least one static language such as C, C++, C#, Java, or Scala
  • Proven track record with deploying, monitoring, and debugging scalable AI/ML models
  • Strong EE fundamentals, including computer architecture, high-speed interfaces, timing, power basics, and a solid understanding of firmware/driver structures and hardware interaction
  • Experience working within a silicon development environment, with exposure to chip and system characterization methodologies
  • Hands-on experience with silicon bring-up, characterization, or lab debug using standard tools (e.g., oscilloscopes, multimeters, logic analyzers)
  • Proven ability to balance multiple simultaneous projects with excellent problem-solving, communication, and collaboration skills

Nice to have

  • modern AI technologies and methodologies for crafting and launching LLMs
  • translate innovative AI research into practical, high-impact production tools
  • deep learning frameworks like PyTorch or TensorFlow
  • agentic and orchestration tools including NeMo Agent Toolkit, LangChain, Semantic Kernel, AutoGen, CrewAI, or n8n
  • debugging complex system-level issues involving HW/SW interactions
  • leadership or ownership in driving root cause analysis of silicon or feature-level issues

What the JD emphasized

  • building and deploying ML/AI systems
  • owning an AI agent, LLM-powered workflow, or intelligent automation system end-to-end
  • deploying, monitoring, and debugging scalable AI/ML models
  • silicon development environment
  • silicon bring-up, characterization, or lab debug

Other signals

  • LLM-Powered Validation Pipelines
  • Cross-Team AI Integration
  • Technology Scouting & Evaluation
  • Impact Measurement & Continuous Improvement
  • building and deploying ML/AI systems
  • owning an AI agent, LLM-powered workflow, or intelligent automation system end-to-end