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.
You will also be eligible for equity and benefits.
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.Skills Required
- BS, MS, or PhD or equivalent experience in CS, EE, CE, or related field with 5+ years building and deploying ML/AI systems or data-intensive backend services
- 2+ years 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 (C, C++, C#, Java, or Scala)
- Proven track record deploying, monitoring, and debugging scalable AI/ML models
- Strong EE fundamentals including computer architecture, high-speed interfaces, timing, power basics, and understanding of firmware/driver structures and hardware interaction
- Experience in a silicon development environment with exposure to chip and system characterization methodologies
- Hands-on experience with silicon bring-up, characterization, or lab debug using tools such as oscilloscopes, multimeters, and logic analyzers
- Proven ability to balance multiple simultaneous projects with strong problem-solving, communication, and collaboration skills
- Familiarity with modern AI technologies and methodologies for crafting and launching LLMs and translating research into production tools
- Experience with deep learning frameworks such as PyTorch or TensorFlow
- Hands-on experience with agentic and orchestration tools (NeMo Agent Toolkit, LangChain, Semantic Kernel, AutoGen, CrewAI, n8n)
- Experience debugging complex system-level HW/SW interactions and leading root cause analysis of silicon or feature-level issues
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
