We9re looking for an ambitious Senior Solutions Architect to drive validation of NVIDIA AI factories from first rack power-on through customer handoff. You will be embedded in launches from the start, running and debugging AI/LLM workloads and benchmarks on Linux-based GPU clusters using NCCL and collectives (AllReduce, AllToAll) to validate performance and scalability. When workloads or benchmarks fall short, you9re the expert who digs in, partners with engineering, and drives resolution.
You will operationalize observability and automation to accelerate validation, capture structured evidence across every bring-up milestone, and work directly with internal deployment teams and external customers to ensure AI factories are ready at launch. Your work directly enables the success of NVIDIA9s first external product launches!
What You Will be Doing:- 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.
What We Need to See:- Bachelor9s 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.
Ways to Stand Out From the Crowd:- Experience with AI factory or large-scale AI infrastructure build, deployment, or operations.
- Background in HPC performance engineering, SRE, or systems performance analysis for GPU-accelerated environments.
- Familiarity with observability stacks (e.g., metrics/monitoring, logging, tracing systems) used for large distributed systems.
- Experience building automation and CI-style pipelines for running and validating benchmarks at scale.
- Demonstrated desire to use AI to solve practical problems, improve workflows, and guide data-driven decisions.
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 148,000 USD - 235,750 USD.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until July 18, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
