NVIDIA
Senior Solutions Architect, First Time Deployment Validation - NVIS
$148K–$236K
US, CA, Santa Clara +3 · Senior · Full-time · Posted Oct 6, 2026
- Location
- US, CA, Santa Clara
US, TX, Remote
US, VA, Remote
US, WA, Remote - Experience
- More than 6+ years managing Linux-based systems in HPC, distributed systems, or extensive AI/ML settings.
- Education
- Bachelor’s degree or equivalent experience in Computer Science, Mathematics, Engineering, Physics, or related field.
About the job
Validates NVIDIA AI factories from initial rack power-on through customer handoff by running and troubleshooting AI workloads and benchmarks, improving observability and automation, and coordinating launch readiness.
What you'll do
- Configure and validate multi-GPU, multi-node Linux clusters and execute, analyze, and troubleshoot AI/LLM benchmarks.
- Build observability and Python/Shell automation for workload monitoring, benchmark results, and regression checks.
- Coordinate with internal teams and customers on performance fixes, readiness documentation, and deployment handoff.
What they ask for
- Bachelor’s degree or equivalent experience in Computer Science, Mathematics, Engineering, Physics, or a related field.
- Hands-on experience running AI/ML workloads on multi-GPU and/or multi-node clusters, with practical knowledge of NCCL and collectives such as AllReduce and AllToAll.
- Proficiency with Python and Shell/Bash; experience benchmarking and interpreting performance results.
- Familiarity with LLM training and/or inference workflows using frameworks such as PyTorch or TensorFlow, and observability data for troubleshooting.
Nice to have
- Experience with AI factory or large-scale AI infrastructure deployment or operations; HPC performance engineering, SRE, or GPU systems performance analysis.
- Familiarity with observability stacks or automation and CI-style benchmark pipelines at scale.
Pay
148,000 USD - 235,750 USD (base salary)
Tools
Linux, NCCL, AllReduce, AllToAll, PyTorch, TensorFlow, Python, Shell/Bash
AI focus
Validates AI/LLM workloads and benchmarks on AI factory clusters; the posting also notes a desire to use AI to solve practical problems, improve workflows, and guide data-driven decisions.
Model-written summary · the original posting is authoritative