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NVIDIA

Designs the chips and software that power AI

Senior Field Applications Engineer - AI Infrastructure Build, Execution

Implementation & Technical Account ManagementConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • A Bachelor's or Master's degree in Electrical Engineering, Computer Engineering, Computer Science, Systems Engineering, or a related technical field, or equivalent experience.
  • At least 5 years of experience in data center deployment, rack integration, technical field support, manufacturing support, partner services, infrastructure bring-up, or large-scale systems troubleshooting.
  • Practical experience troubleshooting intricate infrastructure problems related to servers, GPUs, switches, storage, firmware/BMC, Linux, cabling, optics, power, thermal, or deployment tooling.
  • Experience working directly with partner, customer, OEM, ODM, system integrator, or deployment services teams in field or production environments.
  • Strong field troubleshooting ability, including rigorous debugging, clean data collection, reproduction quality, and clear handoff of critical issues.
  • Ability to operate onsite in fast-paced deployment environments while communicating clearly with engineering, field teams, partner teams, and leadership.
  • Strong written and verbal communication skills, including the ability to summarize complex technical issues into executive-ready status and risk updates.
  • Willingness to travel up to 50% to support field engagements, partner sites, and deployment activities.

Responsibilities

  • Deliver direct field assistance for AI Factory build implementation, including First Time Deployments, deployment bring-up, validation, and issue triage with OEM and deployment partners.
  • Serve as a senior technical resource for urgent field issues involving customer concerns across rack, server, GPU, networking, storage, firmware/BMC, Linux, cabling, power, thermal, and deployment-tooling domains.
  • Deploy to partner or field sites as needed to support targeted rapid response, critical issue closure, and execution risk reduction.
  • Drive structured triage for deployment blockers, including failure reproduction, data collection, domain isolation, quality of critical issue packets, and owner alignment.
  • Help reduce time to resolution by validating diagnostic flows, improving test practices, and detailing repeatable troubleshooting methods.
  • Support bone-pile review and reduction by helping classify failures, identify repeat patterns, and connect field data to root cause and corrective actions.
  • Capture First Time Deployment lessons learned and update guidelines, readiness criteria, and execution playbooks for reuse across OEM engagements.
  • Provide concise leadership access to issue status, risk, blockers, decision needs, and recovery plans during active deployments.

Desired Qualifications

  • Experience with AI Factory, accelerated computing, rack-scale AI infrastructure, large-scale GPU systems, or HPC deployments.
  • Background with NVIDIA GPUs, NVIDIA networking, InfiniBand, RoCE, high-speed Ethernet, or large-scale cluster bring-up.
  • Experience crafting or improving diagnostic flows, test procedures, readiness playbooks, blocking issue templates, or best-practice libraries.
  • Experience with bone-pile analysis, RMA/failure pattern review, root cause feedback loops, or systemic quality improvement.
  • Strong Linux, scripting, automation, telemetry, or log-analysis skills.

About the Company

NVIDIA

Designs the chips and software that power AI

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.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1993

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