Senior Solutions Architect, AI Factory Deployment

Job not on LinkedIn

🕒 July 31

🌐 United States, Canada – Remote

🇺🇸 United States – Remote 🇨🇦 Canada – Remote

🏄 California, North Carolina, +1 more states – Remote

🏄 California – Remote 🌲 North Carolina – Remote 🤠 Texas – Remote

💵 $152k - $287.5k / year

⏰ Full Time

🟠 Senior

💻 Solutions Engineer

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NVIDIA

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1993

🏥 Healthcare

🏭 Manufacturing

🤖 Artificial Intelligence

Healthcare • Manufacturing • Artificial Intelligence

NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

📋 Description

• Set up, adjust, and verify AI factory environments across multi-GPU and multi-node Linux clusters • Validate configurations against guidelines for NCCL, collectives, and distributed training frameworks • Run key AI/LLM benchmarks — setup, orchestration, result collection, and analysis • Investigate and address problems when training jobs or benchmarks fail, hang, or perform below expectations • Build and improve observability for AI factories (metrics, logs, traces, dashboards) to understand workload behavior and system health • Build automation using Python and Shell for conducting benchmarks, retrieving results, and completing regression checks • Analyze communication patterns and NCCL usage for AI/LLM workloads, concentrating on collectives such as AllReduce and AllToAll • Help identify and recommend improvements to job configuration, parallelism strategies, and cluster settings to improve throughput, latency, and scaling efficiency • Work closely with hardware, software, networking, and product teams to prepare AI factories for customer use • Contribute to documentation and readiness materials for internal and customer-facing teams

🎯 Requirements

• Bachelor's degree or equivalent experience in Computer Science, Mathematics, Engineering, Physics or a related field • 5+ years of experience managing Linux-based systems in HPC, distributed systems, or AI/ML environments • Hands-on experience running AI/ML workloads on multi-GPU and/or multi-node clusters, including some exposure to NCCL • Practical knowledge of collective communication patterns like AllReduce and AllToAll, and their application in ML/LLM training • Skilled in Python and Shell/Bash for scripting, automation, and tooling • Strong communication skills and the ability to work effectively with cross-functional teams

🏖️ Benefits

• Eligible for equity and benefits