below marketmost similar roles pay hereabove market
This role pays more than
83%
of similar roles. Most pay
$173,950–$235,750
— the blue band above.
At the midpoint, this role pays about
$236k
versus about
$205k
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
1626 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 1115 roles with salary data.
Senior Solutions Architect, AI and HPC Infrastructure joins an interdisciplinary team to support energy-sector customers, including power generation, oil and gas, and renewables. This role involves serving as a technical advisor for accelerated systems architecture, helping customers design, deploy, and optimize AI infrastructure from datacenters to the edge. You will build repeatable reference architectures, deployment guides, and benchmark reports for large-scale GPU clusters and AI factories. Key responsibilities include conducting architecture reviews, technical deep dives, and production deployment guidance for training, inference, and agentic AI workloads. Required expertise includes distributed systems, high-performance computing, and data center networking technologies like InfiniBand, Ethernet, and RDMA. You must also possess knowledge of cluster orchestration, Kubernetes, Docker, NCCL, and OpenMPI to solve complex infrastructure challenges within the energy sector.
What does a Solutions Architect earn in Remote?
Median $220000 from 61 postings across 16 companies.
Advise energy-sector customers on the design, build-out, and optimization of large-scale GPU clusters and edge deployments.
Lead discovery sessions, architecture reviews, technical deep dives, and production deployment guidance for AI infrastructure.
Support infrastructure design across compute, networking, storage, containers, and data center operations for energy-sector workloads.
Drive adoption of systems monitoring, telemetry, and management tools to improve cluster utilization and reliability.
Build repeatable reference architectures, deployment guides, sizing guidance, and technical playbooks for energy-sector AI infrastructure.
Create technical documentation including whitepapers, blogs, and targeted samples to educate internal teams and customers.
Partner with flagship customers and solution partners to drive adoption of NVIDIA's accelerated computing platforms.
What we're looking for
BS, MS, or PhD in Computer Science, Electrical/Computer Engineering, Physics, or a related field (or equivalent experience).
8+ years of hands-on experience in AI infrastructure, accelerated computing, distributed systems, cloud infrastructure, HPC, or machine learning platforms.
Experience designing, deploying, and operating accelerated computing infrastructure at scale.
In-depth knowledge of AI cluster orchestration, scheduling, automation, CI/CD pipelines, and data center networking technologies like InfiniBand and RDMA.
Familiarity with infrastructure requirements for AI workloads, including distributed training, inference serving, storage performance, and cluster reliability.
Working knowledge of one or more energy sectors, including power generation, oil & gas, transmission and distribution, or renewables.
Excellent presentation, communication, problem-solving, documentation, and collaboration skills.
Experience architecting AI factories, large GPU clusters, or deploying LLM training and RAG workflows (preferred).
GPU
CUDA
Machine Learning
Deep Learning
HPC
Kubernetes
Docker
Slurm
GPGPU
Generative AI
Agentic AI
Data Science
Parallel Computing
Networking
Foundation Models
GPU
CUDA
Machine Learning
Deep Learning
HPC
Kubernetes
Docker
Slurm
GPGPU
Generative AI
Agentic AI
Data Science
Parallel Computing
Networking
Foundation Models