Senior AI Infrastructure Engineer - DGX Cloud

NVIDIA•Santa Clara, CA

2d•$152,000 - $287,500

About The Position

NVIDIA is looking for an outstanding, passionate, and dedicated Senior AI Infrastructure Engineer to join our DGX Cloud group. This engineering role will design, build and maintain large-scale production systems with high efficiency and availability using a combination of software and systems engineering practices. This role demands knowledge across different systems, networking, coding, databases, capacity management, continuous delivery and deployment, and open-source cloud-enabling technologies like Kubernetes and OpenStack. The DGX Cloud SRE at NVIDIA ensures our GPU cloud services deliver maximum reliability and uptime. They carefully prepare and plan changes to the system. They also manage capacity and performance. NVIDIA values diversity, curiosity, problem-solving, and openness. Our team includes people with varied backgrounds and perspectives. We encourage collaboration, big thinking, and risk-taking without blame. We promote self-direction on meaningful projects. We also provide support and mentorship to foster learning and growth.

Requirements

BS degree in Computer Science or a related technical field involving coding (e.g., physics or mathematics), or equivalent experience.
5+ years of experience.
A track record showing a good balance between initiating your own projects, convincing others to collaborate with you, and collaborating well on projects initiated by others.
Background in infrastructure automation and distributed systems architecture focused on building tools to manage large-scale private or public cloud platforms in production.
Experience working with one or more of the following languages: Python, Go, C/C++, Java.
Comprehensive understanding in one or more of Linux, Networking, Storage, and Containers Technologies.
Experience with Public Cloud and Infrastructure as a Code (IAAC) and Terraform.
Distributed system experience.

Nice To Haves

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

Responsibilities

Design, build, deploy, and run internal tooling for large-scale AI training and inferencing platform built on top of cloud infrastructure.
Conduct in-depth performance characterization and analysis on large multi-GPU and multi-node clusters.
Engage in and improve the whole lifecycle of services—from inception and design through deployment, operation, and refinement.
Support services before they become available through activities such as system design consulting, developing software tools, platforms, and frameworks, capacity management, and launch reviews.
Maintain services once they are live by measuring and monitoring availability, latency, and overall system health.
Scale systems sustainably through mechanisms like automation, and evolve systems by pushing for changes that improve reliability and velocity.
Practice sustainable incident response and blameless postmortems.
Be part of an on-call rotation to support production systems.