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This listing doesn't post a salary. Most similar roles pay
$173,781–$235,750.
Based on 240 similar postings.
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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, AI Cluster Performance and Telemetry joins the solutions architecture team to serve as a technical expert at the intersection of hardware and software stacks. This role involves analyzing and optimizing performance for AI, deep learning, and HPC ecosystems by identifying bottlenecks across interconnected GPU, CPU, and networking systems. Key responsibilities include maintaining benchmarking suites, monitoring hardware performance counters, and extracting system telemetry to resolve configuration discrepancies. The candidate must utilize tools such as Perf, eBPF, Prometheus, Grafana, Docker, Kubernetes, SLURM, and Ansible. Required skills include transforming raw logs into structured time series data and dashboards. The work focuses on optimizing multi-GPU communication libraries like NCCL, NVIDIA hardware architectures, and distributed AI training workloads for large-scale high-performance computing environments.
What does a Solutions Architect earn in California?
Median $235750 from 57 postings across 11 companies.
Identify and resolve complex performance bottlenecks across interconnected GPU, CPU, and networking systems.
Maintain robust performance 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 low-level technical performance anomalies into clear, actionable narratives for cross-functional teams.
Optimize distributed AI training workloads 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, particularly 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, Docker Swarm, 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, low-level technical performance anomalies into clear, actionable narratives for cross-functional teams.
Deep knowledge of multi-GPU communication libraries like NCCL, NVIDIA hardware architectures, or optimizing distributed AI training workloads (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