below marketmost similar roles pay hereabove market
This role pays more than
81%
of similar roles. Most pay
$162,000–$235,750
— the blue band above.
At the midpoint, this role pays about
$236k
versus about
$199k
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 Solutions Architect, Generative AI joins the team to support leading consumer internet companies and frontier labs in building foundation models. This role focuses on accelerating customer workloads by designing and optimizing large-scale AI clusters across GPU compute, high-performance networking, storage, and orchestration. You will profile distributed training and inference workloads to identify bottlenecks across GPUs, CPUs, memory, and network fabrics. Key responsibilities include diagnosing complex issues spanning InfiniBand, RoCE, RDMA, NCCL, NVLink, and NVSwitch. You will utilize tools like Kubernetes, Slurm, Python, and shell scripting to develop benchmarking tools and automation. The work involves solving infrastructure reliability and cost challenges by maximizing GPU utilization and throughput for large-scale AI infrastructure and distributed systems.
What does a Solutions Architect earn in California?
Median $235750 from 57 postings across 11 companies.
Maximize GPU utilization and end-to-end workload throughput while reducing infrastructure costs for customers.
Design and optimize large-scale AI clusters across compute, networking, storage, and orchestration layers.
Profile distributed training and inference workloads to identify bottlenecks across the hardware and software stack.
Diagnose complex distributed systems issues spanning InfiniBand, RoCE, RDMA, NCCL, NVLink, and NVSwitch.
Lead proof-of-concepts and performance studies while developing benchmarking tools, automation, and technical collateral.
Partner with internal engineering and product teams to secure design wins based on customer requirements.
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 the hardware and software components of large-scale AI clusters.
Hands-on experience designing, deploying, operating, or troubleshooting high-performance GPU networks using technologies such as InfiniBand, RoCE, or GPUDirect RDMA.
Experience debugging NCCL communication and distributed collective performance, including topology, transport, congestion, routing, and host-level configuration issues.
Experience profiling AI workloads and identifying performance bottlenecks across compute, networking, storage, and orchestration layers.
Experience with cluster schedulers and orchestration platforms such as Kubernetes and Slurm, along with containers and production monitoring systems.
Proficiency with Python, shell scripting, or similar languages for infrastructure automation, benchmarking, and systems troubleshooting.