Senior Solutions Architect, First Time Deployment Validation - NVIS

NVIDIA; Virginia (MN); On-site; USD 148,000 - 235,750; Full time

Job summary

NVIDIA is seeking a senior engineer to design and operate AI factory environments across multi-GPU, multi-node Linux clusters. You will ensure NCCL and collectives configurations align with best practices, run key AI/LLM benchmarks, and analyze results for optimization.

You will develop automation in Python and Shell, improve observability through metrics, logs, and dashboards, and work with cross-functional teams to scale deployments. A strong background in HPC and ML workloads is required.

Qualifications

• Bachelor's degree or equivalent in CS, Engineering, Mathematics, Physics, or related field.
• 6+ years of experience managing Linux-based HPC or AI/ML workloads.
• Hands-on experience with AI/ML workloads on multi-GPU/multi-node clusters; practical NCCL knowledge.
• Solid understanding of AllReduce and AllToAll in ML training.
• Familiarity with PyTorch or TensorFlow for LL(M) workloads.
• Proficiency in Python and Shell scripting for automation.
• Experience benchmarking and interpreting performance metrics.
• Comfort with observability data to troubleshoot distributed systems.
• Strong communication and collaboration across functions.

Responsibilities

• Set up, adjust, and verify AI factory environments on Linux clusters.
• Configure NCCL, collectives, and distributed training workflows.
• Execute, collect, and analyze AI/LLM benchmarks.

Skills

Linux systems

HPC

NCCL

Python

Shell/Bash

Benchmarking

Observability data

PyTorch

TensorFlow

Multi-GPU / Multi-node

AllReduce / AllToAll

Cross-functional collaboration

Education

Bachelor's degree or equivalent in CS/Engineering/Math/Physics

Tools

Python

Shell

NCCL Toolkit

Job description

The First Time Deployment Team owns first-time execution of NVIDIA's latest products and systems; gathering install and bring-up evidence, operationalizing the validation process, documenting blockers and finding solutions to launch AI Factories at scale. Our results are spread across NVIDIA so we can succeed at scale.

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

• 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.

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.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.