Applied Ai Engineer

Details

NVIDIA's Silicon Co-Design Group is seeking an Applied AI Engineer to innovate, develop, and integrate innovative AI solutions into the design and automation infrastructure that powers our chips. Every CPU, GPU, and Tegra SoC NVIDIA has shipped in the past four years passed through our toolchain on its way to production — over 200 product SKUs were optimized during the Blackwell generation alone. Now we're rebuilding that toolchain around AI, and we're looking for the engineer to lead that charge. In this role, you will architect and implement solutions that enhance the efficiency, scalability, and intelligence of our workflows, driving initiatives from concept to deployment. If you combine deep technical expertise with a hands-on approach and an aim to push the boundaries of what's possible, this is your opportunity.

What you’ll be doing

• 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.
• 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.
• Technology Scouting & Evaluation: Evaluate emerging AI frameworks and architectures before the rest of the industry catches on, and make the case for adoption.
• Impact Measurement & Continuous Improvement: Build the data systems that prove what’s working, establish quantitative indicators of AI impact, close performance gaps, and drive iteration across the org.

Requirements

• BS, MS, or PhD (or equivalent experience) in CS, EE, CE, or a related field, with 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 and 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 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.

Ways to stand out from the crowd

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