Senior AI Factory Deployment Architect (Multi-GPU)

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

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.