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Applied AI Engineer

NVIDIA San Francisco, CA

San Francisco, CA

Today

$152k - $288k/yr Full time Agentic Ai AI Ai Agent

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Job Description

NVIDIA’s Silicon Co-Design Group is seeking an Applied AI Engineer to design and implement LLM-powered validation approaches and AI integrations across semiconductor design and automation workflows. The work centers on advancing post-silicon and validation processes from early concept stages through production deployment.

Responsibilities

Design and deploy AI systems that improve the speed, intelligence, and scalability of post-silicon validation across semiconductor environments.
Partner with multi-functional engineering teams across the organization to identify where AI can remove bottlenecks, and then build and deliver the solutions.
Assess emerging AI frameworks and architectures early, before they become widely adopted across the industry.
Build the data systems needed to validate outcomes, define quantitative measures of AI impact, close performance gaps, and support iterative improvement across the organization.

Requirements

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 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.
Strong EE fundamentals, including computer architecture, high-speed interfaces, timing, power basics, and an understanding of firmware/driver structures and hardware interaction.
Hands-on experience in production test, system validation, post-silicon bring-up, reliability, silicon debug, or silicon productization, including ATE, SLT, board-level test, validation, or yield analysis.
Experience in a silicon development environment, including exposure to chip and system characterization methodologies, and familiarity with manufacturing and quality metrics such as yield, FPY, DPPM, RAS, TTR, and escape rate.
Demonstrated ability to balance multiple concurrent projects, with strong problem-solving, communication, and teamwork skills.

Technology Stack

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

Compensation and Location

This role is based in California (hybrid) and offers an estimated USD 152,000 - 287,500 per year.

Benefits

Eligibility for equity and benefits.
Competitive salary.
Generous benefits package.

How You Can Stand Out

Experience with 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, high-impact production tools.
Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow, including agentic and orchestration tools such as NeMo Agent Toolkit, LangChain, Semantic Kernel, AutoGen, CrewAI, or n8n.

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1mo ago

Applied AI Engineer

NVIDIA San Francisco, CA

San Francisco, CA

1mo ago Closed

$152k - $288k/yr Full time Electrical Engineering Agentic Ai AI

This position is no longer accepting applications (closed Aug 30, 2026).

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.

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