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About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
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The Applied AI Engineer joins the Silicon Co-Design Group to innovate, develop, and integrate AI solutions into the design and automation infrastructure for chips. You will architect and implement systems to enhance the efficiency and scalability of workflows, specifically designing LLM-powered validation pipelines for post-silicon validation across semiconductor environments. Key responsibilities include cross-team AI integration, technology scouting, and building data systems to measure impact. Required skills include Python, a static language like C, C++, C#, Java, or Scala, and deep EE fundamentals involving computer architecture and firmware structures. You must have experience in production test, system validation, or silicon debug. The role addresses the technical challenge of rebuilding the silicon toolchain around AI to optimize hardware production and performance.
Design and deploy AI systems to make post-silicon validation faster and more scalable.
Architect and implement solutions to enhance the efficiency and intelligence of silicon design workflows.
Identify friction points in multi-functional engineering teams and build AI solutions to eliminate them.
Evaluate and scout emerging AI frameworks and architectures for potential adoption.
Build data systems to establish quantitative indicators of AI impact and performance.
Translate innovative AI research into practical, high-impact production tools.
Manage the end-to-end lifecycle of AI agents and LLM-powered workflows from prototype to production.
What we're looking for
BS, MS, or PhD in CS, EE, CE, or a related field, or equivalent experience.
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.
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 firmware/driver structures.
Hands-on experience in production test, system validation, post-silicon bring-up, reliability, silicon debug, or silicon productization.
Experience working within a silicon development environment with exposure to chip and system characterization methodologies.
Demonstrated experience with deep learning frameworks like PyTorch or TensorFlow, and hands-on experience with agentic and orchestration tools (preferred).