Senior Infrastructure Automation Engineer
NVIDIA
Senior Infrastructure Automation Engineer
NVIDIA
Designs the chips and software that power AI
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/
NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.
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