Applied AI Engineer

Nvidia

Confirmed live 3 days ago High trust

Remote

Quick summary

Work type: Remote
Location: CAAZFLSanta Clara, CA
Employment: Full-time
Posted: 53 days ago
Freshness: Confirmed live 3 days ago
Nearby: 99+ roles within 25 mi

Market check

Salary context

How this pay compares to similar roles

Similar $193k

$131k $249k

below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $149,462–$237,250.

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 1664 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 653 roles with salary data.

Most-posted roles

View all roles at Nvidia

At a glance

TL;DR · Applied AI Engineer

Role Posting Log in to save

The Applied AI Engineer joins the Silicon Co-Design Group to innovate, develop, and integrate AI solutions into the design and automation infrastructure for chips. You will architect and implement systems to enhance workflow efficiency, specifically designing LLM-powered validation pipelines for post-silicon validation across semiconductor environments. Responsibilities include cross-team AI integration, technology scouting, and building data systems to measure AI impact. Required skills include a degree in CS, EE, or CE, with experience building ML systems, data-intensive backend services, and Python. You must also possess proficiency in C, C++, C#, Java, or Scala, alongside EE fundamentals like computer architecture and hardware interaction. The role involves solving technical problems in silicon development, chip characterization, and debugging complex hardware-software interactions using tools like PyTorch, TensorFlow, and LangChain.

Skills

Python C++ PyTorch TensorFlow LLM LangChain NeMo Agent Toolkit Semantic Kernel AutoGen CrewAI n8n Computer Architecture Firmware Oscilloscope Logic Analyzer C Java Scala

What you'll do

Design and deploy AI systems to make post-silicon validation faster and more scalable.
Build AI-powered workflows and intelligent automation systems from prototype to production.
Identify friction points in multi-functional engineering workflows and build AI solutions to eliminate them.
Evaluate and scout emerging AI frameworks and architectures for potential adoption.
Build data systems to establish quantitative indicators of AI impact and performance.
Translate innovative AI research into practical, high-impact production tools.
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 independently 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 working within a silicon development environment with exposure to chip and system characterization methodologies.
Hands-on experience with silicon bring-up, characterization, or lab debug using standard tools like oscilloscopes and logic analyzers.