below marketmost similar roles pay hereabove market
This role pays less than
52%
of similar roles. Most pay
$144,350–$231,362
— the blue band above.
At the midpoint, this role pays about
$170k
versus about
$188k
for comparable roles.
Based on 240 similar postings.
Employer
About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
1623 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 1110 roles with salary data.
The Senior Field Applications Engineer, AI Infrastructure Build - Execution joins the team to provide on-site technical support for AI Factory build implementations. This role involves assisting OEM and deployment partners with first-time deployments, bring-up, validation, and urgent issue triage. You will identify blockers, validate diagnostic flows, and transform field findings into repeatable guidelines to reduce resolution times. Key responsibilities include troubleshooting complex infrastructure involving racks, servers, GPUs, networking, storage, firmware/BMC, Linux, cabling, power, and thermal systems. You must possess a degree in a technical field and experience in data center deployment or large-scale systems troubleshooting. The role requires proficiency in high-speed Ethernet, InfiniBand, RoCE, and log-analysis to solve critical hardware and software integration problems within accelerated computing and large-scale GPU cluster environments.
Provide on-site technical support for AI Factory build implementation, including first-time deployments and bring-up.
Troubleshoot urgent field issues across GPU, networking, storage, firmware, Linux, and power domains.
Drive structured triage for deployment blockers by reproducing failures and isolating technical domains.
Validate diagnostic flows and develop repeatable troubleshooting methods to reduce time to resolution.
Analyze failure patterns to connect field data to root causes and corrective actions.
Update execution playbooks and readiness criteria based on lessons learned from deployments.
Provide leadership with concise updates on issue status, risks, blockers, and recovery plans.
What we're looking for
Bachelor's or Master's degree in Electrical Engineering, Computer Engineering, Computer Science, Systems Engineering, or a related technical field (or equivalent experience).
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 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 troubleshooting skills including rigorous debug, clean data collection, reproduction quality, and clear handoff of critical issues.
Ability to communicate complex technical issues into executive-ready status and risk updates clearly.
Ability to travel up to 50% to support field engagements and partner sites.
Experience with AI Factory, accelerated computing, rack-scale AI infrastructure, or large-scale GPU systems (preferred).
AI Infrastructure
High-Performance Computing
GPU Systems
Data Center Infrastructure
Accelerated Computing
AI/ML
Rack-scale AI Infrastructure
NVIDIA SDKs
HPC
Deployment Validation
Hardware Engineering
Networking
GPU Architecture
AI Workloads
Linux
Python
C/C++
Java
PyTorch
TensorFlow
ROCm
HIP
CUDA
Docker
Kubernetes
Slurm
Git
InfiniBand
RoCE
RDMA
RCCL
NCCL
gdb
PCIe
NUMA
System Design
Networking
GPU
TCP/IP
RDMA
RoCE
NICs
Switches
Embedded Linux
APIs
Servers
Storage Systems
CPU Architecture
HVAC
Power Distribution
Cabling
Rack Layout
Data Center Hardware
AI
HPC
PyTorch
TensorFlow
JAX
MLperf
Hugging Face
MLOps
Kubernetes
KVM
OpenStack
OpenShift
Python
C/C++
Fortran
CUDA
HIP
OpenACC
OpenMP
Linux
JIRA
Deep Neural Networks