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1d ago

Applied AI Engineer

NVIDIA San Francisco, CA

San Francisco, CA

1d ago

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

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Job Description

Make post-silicon validation smarter and faster. In NVIDIA’s Silicon Co-Design Group, you will architect and implement LLM-powered validation and AI integrations that strengthen semiconductor design and automation workflows. This California (hybrid) role focuses on improving efficiency, scalability, and intelligence from concept through production deployment.

Competitive pay and a strong total rewards package. The estimated salary range is USD 152,000 - 287,500 per year. You will also be eligible for equity and benefits, along with a generous benefits package.

What you’ll do

Design and deploy AI systems that accelerate, scale, and improve post-silicon validation across semiconductor environments.
Partner with multi-functional engineering teams to identify friction points where AI can help, then build and deliver solutions.
Assess emerging AI frameworks and architectures early to evaluate what delivers value before broader industry adoption.
Build the data systems that validate results, define quantitative indicators for AI impact, close performance gaps, and drive iterative improvements across the organization.

What you’ll bring

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 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 understanding of firmware/driver structures and hardware interaction.
Hands-on experience in areas such as production test, system validation, post-silicon bring-up, reliability, silicon debug, or silicon productization (e.g., ATE, SLT, board-level test, validation, or yield analysis).
Experience in silicon development environments, including exposure to chip and system characterization methodologies, plus familiarity with manufacturing and quality metrics such as yield, FPY, DPPM, RAS, TTR, and escape rate.
Track record of balancing multiple concurrent projects with strong problem-solving, communication, and teamwork skills.

Technologies you may work with

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

Ways to stand out

Exposure to 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 production tools with high impact.
Hands-on experience with deep learning frameworks like PyTorch or TensorFlow, plus 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 will join NVIDIA's Silicon Co-Design Group to architect, develop, and deploy AI powered solutions that enhance the design and automation toolchain for silicon development. This position is based in California with hybrid work options, offering a salary range of USD 152,000 to 287,500 per year. The role requires a PhD and at least five years of hands-on experience in building and deploying ML/AI systems or data intensive backend services.

Responsibilities

Develop AI driven validation pipelines to accelerate post-silicon verification, improving speed, intelligence, and scalability across semiconductor environments. This role focuses on building the next generation of capabilities rather than maintaining existing systems.
Collaborate directly with cross functional engineering teams to identify friction points where AI can add value, then design and deliver the solution. The impact will be felt across teams, products, and silicon generations.
Evaluate emerging AI frameworks and architectures ahead of mainstream adoption, advocate for those with clear merit, and build a business case for adoption.
Construct data systems to quantify AI impact, establish clear metrics, close performance gaps, and drive iterative improvements across the organization to convert insights into lasting gains.

Requirements

PhD, or BS/MS, or an equivalent combination in CS, EE, CE, or 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 to production deployment.
Strong Python skills and proficiency in at least one statically typed language such as C, C++, C#, Java, or Scala.
Experience working within 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 experience with silicon bring-up, characterization, or lab debug using standard tools (oscilloscopes, multimeters, logic analyzers).
Solid EE fundamentals, including computer architecture, high-speed interfaces, timing, power basics, and a good understanding of firmware and driver structures and hardware interaction.
Proven ability to manage multiple concurrent projects and apply 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

Benefits

Equity
Benefits
Competitive salaries

Ways to Stand Out

Experience debugging complex system level issues involving hardware and software interactions, including leadership or ownership in root-cause analysis of silicon or feature level issues.
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 managing hundreds to thousands of tools.
Demonstrated experience with deep learning frameworks like PyTorch or TensorFlow, and hands-on work with agentic and orchestration tools including NeMo Agent Toolkit, LangChain, Semantic Kernel, AutoGen, CrewAI, or n8n.

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