AI Infrastructure Engineer

Join a team of engineers at NVIDIA dedicated to scaling the AI infrastructure that powers cutting-edge data science applications.

We solve complex challenges across datacenter hardware, networking, distributed systems, and software deployment.

Responsibilities

• Contribute to a comprehensive platform that automates GPU asset provisioning, configuration, and lifecycle management across cloud providers.
• Build end-to-end automation of data center management, break/fix, and the full span of system upkeep for large-scale Machine Learning systems.
• Implement monitoring and health management capabilities that enable industry-leading reliability, availability, and scalability of GPU assets.
• Harness multiple data streams, ranging from GPU hardware diagnostics to cluster and network telemetry.
• Build automated test infrastructure that we use to qualify distributed systems for operation.
• Partner with engineering teams across NVIDIA to ensure the software integrates seamlessly from the hardware all the way up to the AI training applications.

Requirements

• 5+ years of experience in a similar role working on large-scale production systems.
• BS in Computer Science/Engineering/Physics/Mathematics or other comparable degree, or equivalent experience.
• Expert level knowledge of a systems programming language (Go, Python, and/or Rust) and a solid understanding of Data Structures and Algorithms.
• Strong background in Linux system administration and management.
• Deep knowledge of container orchestration platforms such as Kubernetes and/or SLURM.
• Understanding of performance, security, and reliability in complex distributed systems.
• Familiarity with system level architecture, data synchronization, fault tolerance, and state management.

Preferred Qualifications

• Proficiency in designing and building widespread distributed networks.
• Experience managing large-scale distributed systems independently of cloud providers.
• Extensive expertise in datacenter operations and GPU hardware.
• Hands-on experience working with RDMA networking.
• Advanced hands-on experience and deep understanding of cluster management systems (Kubernetes, SLURM).

You will also be eligible for equity and benefits.