This role pays more than
74%
of similar roles. Most pay
$172,413–$239,606
— the shaded band above.
At the midpoint, this role pays about
$236k
versus about
$206k
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, AI Cluster Performance and Telemetry, you will join the solutions architecture team to serve as a technical expert at the intersection of hardware and complex software stacks. You will analyze and optimize performance for AI, deep learning, and HPC ecosystems by identifying bottlenecks across interconnected GPU, CPU, and networking systems. Your daily responsibilities include maintaining benchmarking suites, monitoring hardware counters, and investigating system configurations to resolve discrepancies affecting peak performance. You will utilize tools such as Perf, eBPF, Prometheus, Grafana, Docker, Kubernetes, SLURM, and Ansible while working with NCCL and NVIDIA architectures like NVLink. The role focuses on the technical challenge of managing high-performance clusters, transforming raw telemetry into structured data, and optimizing distributed AI training workloads and large language models within massive infrastructure environments.
What does a Solutions Architect earn in California?
Median $235750 from 45 postings across 8 companies.
Identify and resolve complex performance bottlenecks across interconnected GPU, CPU, and networking systems.
Develop and maintain robust benchmarking suites to stress-test high-performance clusters and establish baselines.
Use industry-standard tools to monitor hardware performance counters and extract deep system telemetry.
Investigate system and software configurations to identify and fix discrepancies impacting peak performance.
Transform raw logs and telemetry into structured time series data, dashboards, and heat maps.
Translate complex technical performance anomalies into clear, actionable narratives for cross-functional teams.
Optimize distributed AI training workloads, LLMs, and large-scale high-performance computing environments.
What we're looking for
BS or MS in Engineering, Electrical Engineering, Physics, or Computer Science (or equivalent experience).
8+ years of work-related experience in the high-tech industry, specifically in system build, performance analysis, and technical customer-facing roles.
Strong understanding of how CPUs, GPUs, and high-speed networking fabrics interact within massive clusters.
Practical experience with performance counters, profiling tools, and telemetry collection systems like Perf, eBPF, Prometheus, and Grafana.
Practical experience with containers, cloud provisioning, and scheduling tools such as Docker, Kubernetes, SLURM, and Ansible.
Proven track record of transforming raw logs and telemetry into structured time series data, dashboards, and heat maps.
Ability to translate complex technical performance anomalies into clear narratives for cross-functional teams.
Experience with multi-GPU communication libraries (NCCL), NVIDIA hardware architectures, or optimizing distributed AI training workloads.
GPU
CUDA
Machine Learning
Deep Learning
High-Performance Computing
Generative AI
Agentic AI
Docker
Kubernetes
Slurm
GPGPU
Parallel Computing
Data Science
Networking
GPU
CUDA
Machine Learning
Deep Learning
High-Performance Computing
Generative AI
Agentic AI
Docker
Kubernetes
Slurm
GPGPU
Data Science
Parallel Computing
Networking