This role pays more than
71%
of similar roles. Most pay
$167,950–$246,150
— the shaded band above.
At the midpoint, this role pays about
$236k
versus about
$207k
for comparable roles.
Based on 239 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
896 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 876 roles with salary data.
As a Senior Solutions Architect, Generative AI, you will join the team to support leading consumer internet companies and frontier labs building foundation models. You will be responsible for accelerating customer workloads, designing high-performance AI infrastructure, and leading technical engagements regarding NVIDIA technologies. Your daily work involves optimizing large-scale clusters across GPU compute, networking, storage, and orchestration while profiling distributed training and inference workloads to identify bottlenecks. You will diagnose complex issues involving InfiniBand, RoCE, RDMA, NCCL, NVLink, and NVSwitch. Required skills include expertise in Linux systems, GPU architectures, Kubernetes, Slurm, Python, and shell scripting. You will also develop benchmarking tools and automation for infrastructure reliability. The role focuses on solving critical performance and scalability challenges within large-scale AI clusters to improve throughput and reduce costs for high-performance computing environments.
What does a Solutions Architect earn in California?
Median $235750 from 45 postings across 8 companies.
Maximize GPU utilization and end-to-end workload throughput while improving infrastructure reliability and reducing costs for customers.
Design and optimize large-scale AI clusters across compute, networking, storage, scheduling, orchestration, and observability layers.
Profile distributed training and inference workloads to identify bottlenecks across GPUs, CPUs, memory, network fabrics, and software stacks.
Diagnose complex issues in InfiniBand and RoCE fabrics, including RDMA, NCCL, NVLink, and NVSwitch technologies.
Lead proof-of-concepts and performance studies for large-scale AI infrastructure while developing benchmarking tools and automation scripts.
Partner with internal engineering and product teams to secure design wins based on customer requirements and field feedback.
What we're looking for
BS, MS, or PhD in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, or another Engineering field, or equivalent experience.
6+ years of experience in AI infrastructure, systems engineering, high-performance computing, networking, site reliability engineering, or a related technical role.
Deep understanding of Linux systems, distributed computing, GPU architectures, and large-scale AI cluster components.
Hands-on experience designing and troubleshooting high-performance GPU networks using InfiniBand, RoCE, or GPUDirect RDMA.
Experience debugging NCCL communication and distributed collective performance across various network topologies and transport layers.
Experience profiling AI workloads to identify bottlenecks in compute, networking, storage, and orchestration layers.
Experience with cluster schedulers and orchestration platforms such as Kubernetes and Slurm.
Proficiency with Python or shell scripting for infrastructure automation, benchmarking, and systems troubleshooting.