Applied AI Engineer

Make post-silicon validation smarter and faster. In NVIDIA’s Silicon Co-Design Group, you will architect and implement LLM-powered validation and AI integrations that strengthen semiconductor design and automation workflows. This California (hybrid) role focuses on improving efficiency, scalability, and intelligence from concept through production deployment.

Competitive pay and a strong total rewards package. The estimated salary range is USD 152,000 - 287,500 per year. You will also be eligible for equity and benefits, along with a generous benefits package.

What you’ll do

• Design and deploy AI systems that accelerate, scale, and improve post-silicon validation across semiconductor environments.
• Partner with multi-functional engineering teams to identify friction points where AI can help, then build and deliver solutions.
• Assess emerging AI frameworks and architectures early to evaluate what delivers value before broader industry adoption.
• Build the data systems that validate results, define quantitative indicators for AI impact, close performance gaps, and drive iterative improvements across the organization.

What you’ll bring

• BS, MS, or PhD or equivalent experience in CS, EE, CE, or a related field, plus 5+ years of hands-on experience building and deploying ML/AI systems or data-intensive backend services.
• 2+ years of 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 and proficiency in at least one static language such as C, C++, C#, Java, or Scala.
• Strong EE fundamentals including computer architecture, high-speed interfaces, timing, power basics, and understanding of firmware/driver structures and hardware interaction.
• Hands-on experience in areas such as production test, system validation, post-silicon bring-up, reliability, silicon debug, or silicon productization (e.g., ATE, SLT, board-level test, validation, or yield analysis).
• Experience in silicon development environments, including exposure to chip and system characterization methodologies, plus familiarity with manufacturing and quality metrics such as yield, FPY, DPPM, RAS, TTR, and escape rate.
• Track record of balancing multiple concurrent projects with strong problem-solving, communication, and teamwork skills.

Technologies you may work with

• Python, C, C++, C#, Java, Scala
• PyTorch, TensorFlow
• NeMo Agent Toolkit, LangChain, Semantic Kernel, AutoGen, CrewAI, n8n

Ways to stand out

• Exposure to GPU, CPU, AI accelerator, networking, automotive, or other large-scale SoC programs.
• Familiarity with modern AI technologies and methodologies for crafting and launching LLMs.
• Experience building and deploying orchestration agents that manage hundreds to thousands of tools.
• Ability to translate AI research into practical production tools with high impact.
• Hands-on experience with deep learning frameworks like PyTorch or TensorFlow, plus agentic and orchestration tools such as NeMo Agent Toolkit, LangChain, Semantic Kernel, AutoGen, CrewAI, or n8n.