below marketmost similar roles pay hereabove market
This role pays more than
78%
of similar roles. Most pay
$172,925–$235,750
— the blue band above.
At the midpoint, this role pays about
$236k
versus about
$204k
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, AI Factory Observability and Visualization joins the Infrastructure Specialists team to develop full-spectrum visibility for HPC systems and AI factories. This role involves running AI factory validation tools, microbenchmarks, and workloads to interpret system health and performance. You will establish metrics, logs, and signals to define healthy states across the stack, building and extending the telemetry surface for hardware, fabric, and workloads. Key responsibilities include developing automation for data collection and transformation, and collaborating across hardware, software, and networking groups to ready systems for deployment. Required skills include proficiency in Python and Shell, experience with Linux-based distributed systems, and familiarity with observability tools like Prometheus, Grafana, and Loki. You must understand GPU and fabric telemetry, including DCGM, NVLink, and InfiniBand.
What does a Solutions Architect earn in California?
Median $235750 from 57 postings across 11 companies.
Run AI factory validation tools, microbenchmarks, and workloads to assess system health and performance.
Define health metrics, logs, and signals across the hardware, fabric, and workload stack.
Build and extend telemetry surfaces to manage how data is collected, transformed, and stored.
Develop Python and Shell scripts to automate the collection and presentation of system data.
Investigate visibility gaps to ensure telemetry accurately reflects true system behavior.
Recommend improvements for system visibility, data sources, and reporting to provide clearer insights.
Create documentation and readiness materials to prepare HPC systems for customer deployment.
What we're looking for
Bachelor's degree or equivalent experience in Computer Science, Mathematics, Engineering, Physics, or a related field.
6+ years of experience managing Linux-based systems in HPC, distributed systems, or large AI/ML settings.
Hands-on experience with the architecture of multi-GPU and/or multi-node clusters, including networking and interconnects.
Proficiency with Python and Shell/Bash for scripting, automation, and tooling.
Practical experience with observability systems like Prometheus, Grafana, or Loki, including building custom exporters and handling large-scale metrics.
Experience transforming metrics, logs, and traces into actionable insights for complex distributed environments.
Familiarity with GPU and fabric telemetry, such as DCGM, NVLink, and InfiniBand/Ethernet fabric counters.
Experience with AI factory infrastructure build, deployment, or operations (preferred).