below marketmost similar roles pay hereabove market
This role pays more than
73%
of similar roles. Most pay
$183,462–$241,750
— the blue band above.
At the midpoint, this role pays about
$236k
versus about
$213k
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
1664 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 653 roles with salary data.
The Senior Solutions Architect, NVIDIA AI Factory Infrastructure joins the team to drive technical engagement with customers across AI Factory teams. This role involves mapping complex, large-scale customer workloads to the NVIDIA technology stack to achieve measurable outcomes. You will co-design advanced, scalable architectures, define technical directions, and create deployment plans while serving as a trusted advisor. Key responsibilities include leading technical engagements, resolving sophisticated issues, and partnering with engineering and product teams to ensure successful solution deployment. Required skills include deep technical expertise in AI High Performance Computing, DataCenter design, and architecture. You must demonstrate proficiency in software or hardware engineering, technical partnerships, and solutions architecture. Notable domain context includes practical knowledge of DGX, NVL72, MGX, and HGX systems, as well as Datacenter power, cooling, and rack placements.
What does a Solutions Architect earn in California?
Median $235750 from 37 postings across 11 companies.
Map complex customer workloads to NVIDIA’s technology stack to achieve measurable outcomes.
Co-design advanced, scalable architectures and define technical deployment plans with customers.
Design, validate, and support technical solutions to ensure customer success.
Lead technical engagements to resolve sophisticated issues and advocate for customer needs.
Partner with engineering and product teams to ensure successful deployment and continuous improvement.
Provide technical expertise to sales account managers to secure build wins.
Cultivate technical relationships with engineers, management, and architects at key customer accounts.
What we're looking for
Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent experience.
8+ years of professional experience in software/hardware engineering, developer relations, technical partnerships, solutions architecture, or product management.
3-5+ years of direct, hands-on experience in DataCenter design and architecture.
Deep technical expertise in AI High Performance Computing with a track record of driving product integrations.
Ability to communicate sophisticated technical ideas clearly to both technical and non-technical audiences.
Proven success in balancing customer needs with the ability to drive complex product decisions through positive relationships.
Practical knowledge of NVIDIA systems technology, such as DGX, NVL72, MGX and HGX systems (preferred).
Experience working with OEMs in the Datacenter ecosystem and understanding of DC power, cooling, and rack placements (preferred).
GPU
HPC
AI
ML
GenAI
NVIDIA Hardware
NCCL
DCGM
UFM
Mission Control
Base Command Manager
SLURM
K8s
Data Center Design
Cluster Administration
Performance Testing
AI Benchmarking
GPU Infrastructure
HPC
GenAI
AI
ML
NVIDIA Hardware
NCCL
DCGM
UFM
Mission Control
Base Command Manager
SLURM
Networking
Storage
Data Center Design
Performance Testing
AI Benchmarking
Cluster Administration
Generative AI
Large Language Models
GPU
HPC
SLURM
NCCL
DCGM
UFM
Mission Control
Base Command Manager
Deep Learning
Cloud Infrastructure
Networking
Performance Analysis
AI Benchmarking
Data Center Design
NVIDIA AI Factory
Kubernetes
Slurm
MaaS
NVIDIA Base Command Manager
Cluster Management
Workload Orchestration
System Firmware
Operating Systems
PCIe
Network Topologies
On-premises AI
System Architecture
Hardware Infrastructure
SDKs
Frameworks