Senior Field Applications Engineer, AI Infrastructure Build - Execution

NVIDIA•Houston, TX

9d•$132,000 - $253,000

About The Position

NVIDIA is seeking a Senior Field Applications Engineer to support AI Factory build implementation and provide rapid assistance in collaboration with OEMs and deployment partners. The role delivers practical, on-site technical support for initial deployments, critical field challenges, preparation shortfalls, and recurring build issues requiring focused resolution. The engineer works with NVIDIA engineering, services, architecture, program, account, field, and partner-facing teams to identify issues, speed resolution, validate diagnostic flows, and turn field findings into reusable guidelines. This is a senior, field-facing technical contributor role, with onsite support encouraged during critical build, bring-up, validation, and blocking-issue periods. Travel of up to 50% is required. The base salary range is $132,000–$207,000 for Level 3 and $160,000–$253,000 for Level 4. The role is eligible for equity and benefits. Applications will be accepted at least until October 18, 2026. This posting is for an existing vacancy.

Requirements

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 complex infrastructure issues involving servers, GPUs, switches, storage, firmware/BMC, Linux, cabling, optics, power, thermal systems, 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 skills, including rigorous debugging, reliable data collection, quality failure reproduction, and clear handoff of critical issues.
Ability to work onsite in fast-paced deployment environments and communicate clearly with engineering, field, partner, and leadership teams.
Strong written and verbal communication skills, including the ability to summarize complex technical issues in executive-ready status and risk updates.
Willingness to travel up to 50% for field engagements, partner sites, and deployment activities.

Nice To Haves

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 creating 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.

Responsibilities

Provide 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 customer field issues involving racks, servers, GPUs, networking, storage, firmware/BMC, Linux, cabling, power, thermal systems, and deployment tooling.
Travel to partner or field sites as needed to provide rapid response, close critical issues, and reduce execution risk.
Lead structured triage of deployment blockers, including failure reproduction, data collection, domain isolation, critical-issue packet quality, and owner alignment.
Validate diagnostic flows, improve test practices, and document repeatable troubleshooting methods to reduce time to resolution.
Support bone-pile reviews and reduction by classifying failures, identifying recurring patterns, and connecting field data to root causes 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 updates on issue status, risks, blockers, decisions needed, and recovery plans during active deployments.