NVIDIA's Silicon Co-Design Group is seeking a Senior Infrastructure Automation 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:
Define clear vision and roadmap for productivity efficiency improvement solutions in alignment with business needs and drive execution from design through delivery.
Lead cross-function engineering teams on project deliverables commitment to streamline the system design and verification process and workflow. You own the pipelines between tools.
Build production-grade AI-assisted automation infrastructure for Silicon Co-Design use cases, including reliable orchestration, observability, evaluation, guardrails, and human-in-the-loop controls where needed.
Drive Cross-function Collaboration with ASIC, SW, System Design, Product, Security, and Operations teams to ensure reliability, scalability, and performance, fostering a culture of technical excellence, collaboration, and ownership.
Drive hard debugging and root-cause analysis across infrastructure, automation, data, and HW/SW boundary issues; separate competing hypotheses, define measurement plans, and converge teams on the right fix.
What we need to see:
MS or PHD in EE or equivalent experience.
Strong software engineering background with 8+ years significant experience designing large-scale infrastructure, framework architecture, or developer platforms.
Strong proficiency in Python and at least one static language (C, C++, C#, Java, Scala, etc).
Hands-on experience in AI/ML and data analysis, preferably with exposure to large-scale datasets.
Strong EE fundamentals, including computer architecture, high-speed interfaces, timing, power basics, and a solid understanding of firmware/driver structures and hardware interaction.
Excellent problem-solving, communication, and collaboration skills.
Ability to break down ambiguous technical problems, explain failure modes and edge cases, and make strong tradeoff decisions under schedule, coverage, quality, and performance constraints.
Track record of independently driving complex, cross-functional work to closure with clear ownership and strong collaboration.
Ways to stand out from the crowd:
Hands-on experience with silicon bring-up, characterization, or lab debug using standard tools (e.g., oscilloscopes, multimeters, logic analyzers).
Experience on building production AI workflow systems for engineering or infrastructure use cases, not just prototypes.
Strong debugging instincts across distributed systems, automation pipelines, and HW/SW interactions, and can explain your hypothesis tree, measurement plan, tradeoffs, and final decision points.
Track record of AI Experience to accelerate coding, analysis, validation, or triage with strong engineering judgment and validation discipline
Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/
Skills Required
- MS or PhD in Electrical Engineering or equivalent experience
- 8+ years experience designing large-scale infrastructure, framework architecture, or developer platforms
- Strong proficiency in Python
- Proficiency in at least one static language (C, C++, C#, Java, Scala)
- Hands-on experience in AI/ML and data analysis, exposure to large-scale datasets
- Strong EE fundamentals: computer architecture, high-speed interfaces, timing, power basics
- Solid understanding of firmware/driver structures and hardware interaction
- Excellent problem-solving, communication, and collaboration skills
- Ability to break down ambiguous technical problems, define measurement plans, and make tradeoff decisions under constraints
- Track record of independently driving complex, cross-functional projects to closure with ownership
- Hands-on silicon bring-up, characterization, or lab debug using oscilloscopes, multimeters, logic analyzers
- Experience building production AI workflow systems for engineering or infrastructure use cases
- Strong debugging instincts across distributed systems, automation pipelines, and HW/SW interactions
- Proven AI experience to accelerate coding, analysis, validation, or triage
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.”
