Applied AI Engineer

Nvidia

Confirmed live yesterday High trust

Hybrid

Quick summary

Work type: Hybrid
Location: —
Salary: $152,000–$241,500 / yr
Posted: 9 days ago
Freshness: Confirmed live yesterday

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Salary context

Competitive pay

How this pay compares to similar roles

Similar $182k

This role $197k

$118k most similar roles pay here $255k

This role pays more than 64% of similar roles. Most pay $144,119–$220,037 — 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 908 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 890 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 silicon products. This role involves architecting and implementing systems to enhance the efficiency of workflows, specifically designing LLM-powered validation pipelines for post-silicon environments and building data systems to measure impact. The engineer will scout emerging frameworks, collaborate with multi-functional teams to eliminate friction, and develop intelligent automation tools. Required skills include Python, at least one static language like C, C++, C#, Java, or Scala, and deep engineering fundamentals in computer architecture and firmware. Candidates must have experience with PyTorch, TensorFlow, and orchestration tools such as LangChain or NeMo Agent Toolkit. The role addresses the technical challenge of rebuilding silicon toolchains around AI to improve scalability across various chip architectures.

Skills

Python C++ PyTorch TensorFlow LLM LangChain NeMo Agent Toolkit Semantic Kernel AutoGen CrewAI n8n Computer Architecture Firmware Silicon Debug ATE SLT SoC Hardware Interaction Data Systems

What you'll do

Design and deploy LLM-powered systems to accelerate post-silicon validation across semiconductor environments.
Architect and implement AI solutions to improve the efficiency and scalability of silicon design workflows.
Identify opportunities for AI integration across multi-functional engineering teams to eliminate operational friction.
Evaluate emerging AI frameworks and architectures to determine their viability for production adoption.
Build data systems to measure the quantitative impact of AI initiatives and drive continuous improvement.
Translate innovative AI research into practical, high-impact tools for silicon productization.
Develop intelligent automation systems and agentic workflows from prototype through production deployment.

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.
Strong EE fundamentals including computer architecture, high-speed interfaces, timing, power basics, and firmware/driver structures.
Hands-on experience in production test, system validation, post-silicon bring-up, reliability, silicon debug, or silicon productization.
Experience working within a silicon development environment with exposure to chip characterization and manufacturing metrics.
Exposure to GPU, CPU, AI accelerator, networking, automotive, or other large-scale SoC programs (preferred).

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