Senior Solutions Architect, AI Factory Deployment - NVIS

About the role

We are seeking a curious and motivated Senior Solutions Architect to join our NVIDIA Infrastructure Specialists team. In this role, you'll support the creation, implementation, and verification of AI factories, focusing on running and debugging AI/LLM workloads and benchmarks on Linux-based GPU clusters. You'll engage with NCCL and collectives like AllReduce and AllToAll to boost performance and scalability, receiving mentorship from senior architects. You will apply observability and automation to improve benchmarking and validation efforts, troubleshoot workloads, and collaborate with various NVIDIA teams to prepare AI factories for customers.

Responsibilities

• 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, focusing 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 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.

Skills

Ways to stand out:

• Experience benchmarking distributed systems — crafting, running, and interpreting performance benchmarks.
• Background in HPC performance engineering, SRE, or systems performance analysis for GPU-accelerated environments.
• Familiarity with observability stacks (metrics/monitoring, logging, tracing) used for large distributed systems.
• Experience building automation and CI-style pipelines for running and validating benchmarks at scale.
• Demonstrated interest in using AI to solve practical problems, improve workflows, and guide data-driven decisions.

Pay

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is:

• 152,000 USD - 241,500 USD for Level 3
• 184,000 USD - 287,500 USD for Level 4

You will also be eligible for equity and benefits.