Senior AI Infrastructure Engineer - DGX Cloud

NVIDIA•Santa Clara, CA

2d•$152,000 - $287,500

About The Position

NVIDIA is hiring a Senior AI Infrastructure Engineer for its DGX Cloud group. The role designs, builds, deploys, operates, and maintains efficient, highly available, large-scale production systems for AI training and inferencing on cloud infrastructure. It combines software and systems engineering across areas such as networking, coding, databases, capacity management, continuous delivery and deployment, and cloud-enabling technologies including Kubernetes and OpenStack. The engineer supports the reliability and uptime of GPU cloud services through careful change planning, capacity and performance management, monitoring, automation, and incident response. The role includes an on-call rotation. NVIDIA describes a collaborative environment that values diversity, curiosity, problem-solving, openness, self-direction, mentorship, and learning.

Requirements

Bachelor's degree in Computer Science or a related technical field involving coding, such as physics or mathematics, or equivalent experience.
At least 5 years of experience.
A track record balancing initiation of personal projects, gaining others' collaboration, and contributing effectively to projects initiated by others.
Background in infrastructure automation and distributed systems architecture, focused on production tools for managing large-scale private or public cloud platforms.
Experience with one or more of Python, Go, C/C++, or Java.
Comprehensive understanding of one or more of Linux, networking, storage, and container technologies.
Experience with public cloud, Infrastructure as Code (IaC), and Terraform.
Distributed systems experience.

Nice To Haves

Interest in crafting, analyzing, and fixing large-scale distributed systems.
Systematic problem-solving approach, strong communication skills, ownership, and drive.
Ability to identify issues, improve code performance, and automate routine tasks.
Experience operating or handling large private and public cloud systems based on Kubernetes or Slurm.

Responsibilities

Design, build, deploy, and operate internal tooling for a large-scale AI training and inferencing platform built on cloud infrastructure.
Conduct performance characterization and analysis on large multi-GPU and multi-node clusters.
Contribute to the full service lifecycle, from inception and design through deployment, operation, and refinement.
Support services before launch through system design consulting, development of software tools, platforms, and frameworks, capacity management, and launch reviews.
Measure and monitor service availability, latency, and overall system health after launch.
Scale systems sustainably through automation and improve reliability and delivery velocity through system changes.
Participate in sustainable incident response and blameless postmortems.
Participate in an on-call rotation to support production systems.