Applied AI Engineer

NVIDIA; San Francisco (CA); Hybrid; USD 152,000 - 288,000; Full time

Benefits offered by this job

Equity

Benefits

Competitive salaries

Job summary

NVIDIA’s Silicon Co-Design Group seeks an Applied AI Engineer to architect, develop, and deploy AI-powered solutions that speed up silicon design and automation toolchains. You will lead LLM-powered validation pipelines and collaborate across teams to scale AI across products and silicon generations.

The role requires a PhD or equivalent with 5+ years in ML/AI systems, strong Python and statically typed language skills, and hands-on silicon experience.

Qualifications

• PhD in CS, EE, CE or related field, or equivalent experience, with 5+ years of 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.

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.

Skills

Python

C/C++

Java

Scala

ML/AI system development

AI agent / LLM workflow

Education

PhD in CS or ECE or related field

Tools

PyTorch

TensorFlow

NeMo Agent Toolkit

LangChain

Semantic Kernel

AutoGen

CrewAI

n8n

Oscilloscopes

Multimeters

Logic Analyzers

Job description

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.