Senior Solutions Architect, Physical AI Cloud at NVIDIA Corporation
Santa Clara, CA
$152,000–$287,500from the description
Aug 9, 2026
Santa Clara, CA
Sep 24, 2026
What this job asks for AI summary
A Solutions Architect role at NVIDIA focused on designing and scaling Kubernetes-native infrastructure for distributed Physical AI and robotics workloads — spanning simulation, synthetic data generation, model training, and inference. The position involves hands-on technical engagement with enterprise partners to translate requirements into GPU-accelerated, cloud-native architectures, and to drive adoption of NVIDIA's AI platforms at scale. Suited to engineers with deep backend infrastructure and AI/ML systems experience.
Senior level · 5+ years · Full-time
“or” means any one of them counts — you don't need all of them.
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How this req sits in the market our data
Rare in this occupation — lead with these, and say what you built with them.
What the occupation pays Median $138,970 (middle half $107,524–$175,762). This posting is about at that midpoint.
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Aug 11, 2026. It is a model, not a headcount.
Why we read it this way (8)
The posting covers two salary bands: Level 3 ($152,000–$241,500) and Level 4 ($184,000–$287,500). The min and max reported here span both levels combined; the actual offer will depend on which level the candidate is placed at.
No specific work location is stated in the posting, so no CBSA or state could be determined. The role may be location-flexible within the US.
SOC classification is Medium confidence: the role is titled Solutions Architect and involves significant customer-facing and design work, but the day-to-day duties are heavily hands-on infrastructure and platform engineering (building and scaling Kubernetes-native GPU environments), which aligns most closely with Software Developers (15-1252). Network and Computer Systems Administrators (15-1244) is the runner-up given the infrastructure operations emphasis.
NIM, TensorRT-LLM, vLLM, and SGLang appear in the required section as inference acceleration technologies; they are marked required accordingly.
Workflow orchestration tools (Airflow, Argo) are listed in the required qualifications section; Argo is captured as an alternative to Airflow since the posting uses 'etc.' framing but names both.
OSMO is mentioned in the required responsibilities narrative rather than a qualifications gate, so it is marked preferred.
All 'Ways to stand out from the crowd' items (ROS2, Isaac Lab, Isaac Sim, GR00T, Cosmos, Dynamo, Triton, S3, NFS, Lustre, REST, gRPC) are explicitly framed as differentiators, not requirements.
Ignored 1 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): GPU workload orchestration.
Read the full posting
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