Applied AI Engineer

Nvidia Corporation in; Santa Clara (CA); On-site; USD 184,000 - 288,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA is seeking an Applied AI Engineer in Finance to architect and deploy AI solutions across silicon design workflows. You will lead the transition of the toolchain to AI-enabled processes, driving efficiency, scalability, and intelligence from concept to deployment.

Expect hands-on work with LLMs, AI frameworks, and hardware-in-the-loop validation, collaborating across teams to deliver impactful production tooling and measurable improvements.

Qualifications

• 5+ years building and deploying ML/AI systems or data-intensive backends.
• 2+ years owning an AI agent or LLM-powered workflow end-to-end.
• Strong Python skills; proficient in C/C++/Java/C#.
• Experience deploying, monitoring, and debugging scalable AI/ML models.
• Solid EE fundamentals, hardware interaction, and system-level knowledge.
• Experience in silicon development environments and lab bring-up.

Responsibilities

• Design and implement LLM-powered validation pipelines for faster silicon validation.
• Collaborate with cross-functional teams to integrate AI into workflows.
• Evaluate emerging AI frameworks and architectures for production use.
• Build data systems to measure AI impact and drive continuous improvement.

Skills

Python

ML/AI systems

C/C++

Communication & collaboration

Hardware interaction

Education

BS/MS/PhD in CS/EE/CE

Tools

PyTorch

TensorFlow

LangChain

NeMo Agent Toolkit

n8n

Semantic Kernel

AutoGen

CrewAI

Job description

Applied AI Engineer (Finance)

NVIDIA's Silicon Co-Design Group is seeking an Applied AI Engineer to innovate, develop, and integrate innovative AI solutions into the design and automation infrastructure that powers our chips. Every CPU, GPU, and Tegra SoC NVIDIA has shipped in the past four years passed through our toolchain on its way to production - over 200 product SKUs were optimized during the Blackwell generation alone. Now we're rebuilding that toolchain around AI, and we're looking for the engineer to lead that charge. In this role, you will architect and implement solutions that enhance the efficiency, scalability, and intelligence of our workflows, driving initiatives from concept to deployment. If you combine deep technical expertise with a hands-on approach and an aim to push the boundaries of what's possible, this is your opportunity. At NVIDIA, we strive for perfection, encourage innovation, and provide opportunities to explore new ways to succeed!

What you'll be doing:

• LLM-Powered Validation Pipelines: Design and deploy AI systems that make post-silicon validation faster, smarter, and more scalable across semiconductor environments. You're not maintaining what exists, you're building what comes next.
• Cross-Team AI Integration: Work directly with multi-functional engineering teams across the organization to identify where AI can eliminate friction, and then build the solution. Your output will be felt across teams, products, and generations of silicon.
• Technology Scouting & Evaluation: Evaluate emerging AI frameworks and architectures before the rest of the industry catches on. Be the person who spots what's worth adopting, and makes the case for it.
• Impact Measurement & Continuous Improvement: Build the data systems that prove what's working. Establish clear, quantitative indicators of AI impact, close performance gaps, and drive iteration across the org to turn insight into lasting improvement!

What we need to see:

• BS, MS, or PhD or equivalent experience in CS, EE, CE, or a 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 through production deployment.
• 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 a solid understanding of firmware/driver structures and hardware interaction.
• 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 (e.g., oscilloscopes, multimeters, logic analyzers).
• Proven ability to balance multiple simultaneous projects with excellent problem-solving, communication, and collaboration skills.

Ways to stand out from the crowd:

• Familiarity with modern AI technologies and methodologies for crafting and launching LLMs with ability to translate innovative AI research into practical, high-impact production tools.
• Ability to translate innovative AI research into practical, high-impact production tools.
• Demonstrated experience with deep learning frameworks like PyTorch or TensorFlow, and hands-on experience with agentic and orchestration tools including NeMo Agent Toolkit, LangChain, Semantic Kernel, AutoGen, CrewAI, or n8n.
• Experience debugging complex system-level issues involving HW/SW interactions, including leadership or ownership in driving root cause analysis of silicon or feature-level issues.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4.

You will also be eligible for equity and benefits .

Applications for this job will be accepted at least until August 1, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.