Applied AI Engineer

Nvidia

Confirmed live yesterday High trust

Remote

Quick summary

Work type: Remote
Location: Santa Clara, CA
Salary: $152,000–$241,500 / yr
Posted: 7 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

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Salary context

Competitive pay

How this pay compares to similar roles

Similar $182k

This role $197k

$121k most similar roles pay here $254k

This role pays more than 64% of similar roles. Most pay $144,575–$219,275 — the shaded band above. At the midpoint, this role pays about $197k versus about $182k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 910 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 894 roles with salary data.

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View all roles at Nvidia

At a glance

TL;DR · Applied AI Engineer

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The Applied AI Engineer joins the Silicon Co-Design Group to innovate and integrate AI solutions into the design and automation infrastructure for chip production. This role involves architecting and implementing systems to enhance workflow efficiency, building LLM-powered validation pipelines for semiconductor environments, and scouting emerging AI frameworks. The engineer will collaborate with multi-functional teams to identify friction points and develop intelligent automation tools while establishing data systems to measure impact. Required skills include Python, at least one static language like C, C++, C#, Java, or Scala, and experience with PyTorch, TensorFlow, LangChain, or NeMo Agent Toolkit. Candidates must possess strong electrical engineering fundamentals, including computer architecture and firmware structures, alongside expertise in silicon bring-up and characterization to solve complex hardware and software interaction challenges within the semiconductor development lifecycle.

Skills

Python C++ PyTorch TensorFlow LLM LangChain NeMo Agent Toolkit Semantic Kernel AutoGen CrewAI n8n Computer Architecture Firmware Logic Analyzers Oscilloscopes

What you'll do

Design and deploy AI systems to automate and scale post-silicon validation processes.
Architect and implement solutions to improve the efficiency of silicon design and automation workflows.
Identify opportunities for AI integration across multi-functional engineering teams to reduce operational friction.
Evaluate emerging AI frameworks and architectures to determine their suitability for production adoption.
Build data systems to measure, quantify, and report the impact of AI initiatives.
Translate innovative AI research into practical, high-impact tools for silicon development.
Develop LLM-powered workflows and intelligent automation systems from prototype through deployment.
Debug complex system-level issues involving hardware and software interactions.

What we're looking for

BS, MS, or PhD in CS, EE, CE, or a related field, or equivalent experience.
5+ years of hands-on experience building and deploying ML/AI systems or data-intensive backend services.
2+ years of direct Applied AI experience owning an AI agent, LLM-powered workflow, or intelligent automation system end-to-end.
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 firmware/driver structures.
Experience in silicon development environments, including chip characterization and lab debug using oscilloscopes, multimeters, or logic analyzers.
Familiarity with LLM research translation, PyTorch, TensorFlow, and agentic orchestration tools like LangChain or CrewAI (preferred).

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