Applied AI Engineer

The Applied AI Engineer role within NVIDIA's Silicon Co-Design Group focuses on architecting, developing, and deploying AI-powered solutions to enhance the silicon design and automation toolchain.

Responsibilities

• LLM powered validation pipelines: design and deploy AI systems that accelerate post-silicon validation across semiconductor environments, prioritizing the creation of next-generation capabilities over maintaining existing ones.
• Cross-team AI integration: collaborate with multi-functional engineering teams to identify friction points where AI can add value, then build scalable solutions with broad impact across products and silicon generations.
• Technology scouting and evaluation: assess emerging AI frameworks and architectures, making a compelling case for those worth adopting ahead of industry peers.
• Impact measurement and continuous improvement: develop data systems to quantify AI impact, establish clear performance indicators, close gaps, and drive iterative improvements across the organization.

Requirements

• PhD in CS, Electrical Engineering, Computer Engineering, or a related field, or equivalent experience, with 5+ years of hands-on ML/AI system development 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 to production deployment.
• Strong Python skills and proficiency in at least one statically typed language such as C, C++, C#, Java, or Scala.
• Experience in a silicon development environment with exposure to chip and system characterization methodologies, process variation, statistical error rates, or advanced timing and power analysis.
• Hands-on silicon bring-up, characterization, or lab debugging using standard tools (e.g., oscilloscopes, multimeters, logic analyzers).
• Solid EE fundamentals including computer architecture, high-speed interfaces, timing, power basics, and understanding of firmware/driver structures and hardware interaction.
• Proven ability to balance multiple concurrent projects with strong problem-solving, communication, and teamwork skills.

Technologies

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

Compensation & Location

• Location: California, hybrid
• Salary: USD 152,000 - 287,500 per year
• Minimum experience: 5 years
• Education: PhD

Benefits

• Equity
• Benefits
• Competitive salaries

Ways to Stand Out From the Crowd

• Experience debugging complex system-level issues involving hardware and software interactions, with leadership or ownership in root-cause analysis of silicon or feature-level problems.
• Ability to translate innovative AI research into practical, high-impact production tools.
• 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.
• Hands-on experience with deep learning frameworks like PyTorch or TensorFlow, and practical use of agentic and orchestration tools including NeMo Agent Toolkit, LangChain, Semantic Kernel, AutoGen, CrewAI, or n8n.