Senior Solutions Architect, First Time Deployment Validation - NVIS
NVIDIA· Architect, Professional Services
Santa Clara, CA, Hybrid (Remote, TX), Hybrid (Remote, VA), Hybrid (Remote, WA)$148k - $235.8kFull-timePosted 3mo ago
About the role
Senior Solutions Architect focused on validating NVIDIA AI factories from initial rack power-on to customer handoff, executing AI/LLM benchmarks on Linux GPU clusters, building observability and automation, and ensuring readiness for first external product launches.
Responsibilities
- Set up, adjust, and verify AI factory environments across multi-GPU and multi-node Linux clusters
- Ensure configurations align with guidelines for NCCL, collectives, and distributed training frameworks
- Own the execution of key AI/LLM benchmarks, including setup, orchestration, result collection, and analysis
- Investigate and resolve issues when training jobs or benchmarks fail, hang, or underperform
- Build and improve observability for AI factories (metrics, logs, traces, dashboards) to understand workload behavior and system health
- Develop automation (Python, Shell) for running benchmarks, collecting results, and performing regression checks
- Examine communication patterns and NCCL usage for AI/LLM workloads, concentrating on collectives such as AllReduce and AllToAll
- Recommend changes to job configuration, parallelism strategies, and cluster settings to improve throughput, latency, and scaling efficiency
- Work closely with hardware, software, networking, datacenter, and product teams to prepare AI factories for customer use
- Contribute to documentation, guidelines, and readiness collateral that support internal collaborators and customer-facing teams
Qualifications
- Bachelor’s degree or equivalent experience in Computer Science, Mathematics, Engineering, Physics, or related field
- More than 6+ years of experience managing Linux-based systems in HPC, distributed systems, or extensive AI/ML settings
- Hands-on experience running AI/ML workloads on multi-GPU and/or multi-node clusters, with practical knowledge of NCCL
- Solid grasp of collective communication patterns, particularly AllReduce and AllToAll, and how they are applied in contemporary ML/LLM training
- Familiarity with LLM training and/or inference workflows using frameworks such as PyTorch or TensorFlow
- Proficiency with Python and Shell/Bash for scripting, automation, and tooling
- Experience with benchmarking (crafting, executing, and interpreting performance benchmarks)
- Comfortable working with observability data (metrics, logs, dashboards) to troubleshoot and optimize complex distributed workloads
- Strong communication skills and the ability to work effectively with cross-functional teams
Skills
Required:Linux system administrationNCCL and collectivesMulti-GPU / Multi-node orchestrationPythonShell/BashBenchmarkingCommunication
Nice to have:Observability tooling
Benefits
- equity
- benefits (see company page)
Last updated Oct 9, 2026. Interested? Apply on the company site.
About NVIDIA
World Leader in AI Computing
NVIDIA invents the GPU and drives advances in AI, HPC, gaming, creative design, autonomous vehicles, and robotics.
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