Senior Solutions Architect - AI Factory Deployment

NVIDIA • Durham, NC

2d • Onsite

About The Position

We are seeking an ambitious Senior Solutions Architect - AI Factory Deployment to join our NVIDIA Infrastructure Specialists team in Santa Clara! This role is uniquely positioned to develop, deploy, and validate AI factories end to end. You will focus on running and debugging AI/LLM workloads and benchmarks on Linux-based GPU clusters, using NCCL and collectives like AllReduce and AllToAll to improve performance and scalability. As part of our world-class team, you will bring to bear observability and automation to improve benchmarks and validation. You will serve as the expert when workloads or benchmarks do not perform flawlessly. You will collaborate across NVIDIA to ensure AI factories are prepared for customers, validating hardware and software for modern AI deployments.

Requirements

• 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.

Nice To Haves

• 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.

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.

Benefits

• equity
• benefits

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Number of Employees: 5,001-10,000 employees