GPU and HPC Infrastructure Engineer - New College Grad 2025

For two decades, we have pioneered visual computing, the art and science of computer graphics. With the invention of the GPU - the engine of modern visual computing - the field has expanded to encompass video games, movie production, product design, medical diagnosis and scientific research. Today, we stand at the beginning of the next era, the AI computing era, ignited by a new computing model, GPU deep learning.

What you will be doing:

• We have built a comprehensive platform that automates GPU asset provisioning, configuration, and lifecycle management across cloud providers. You'll contribute to this platform to build end-to-end automation of datacenter operations, break/fix, and lifecycle management for large-scale Machine Learning systems.

• Implement monitoring and health management capabilities that enable industry-leading reliability, availability, and scalability of GPU assets. You will be harnessing multiple data streams, ranging from GPU hardware diagnostics to cluster and network telemetry.

• Work on software that manages NVLINK topography across GPU clusters.

• Build automated test infrastructure that we use to qualify distributed systems for operation.

• Work with engineering teams across NVIDIA to ensure your software integrates seamlessly from the hardware all the way up to the AI training applications.

• You'll be constantly innovating, discovering new problems and their solutions.

What we need to see:

• Pursuing or recently completed a BS or MS in Computer Science/Engineering/Physics/Mathematics or other comparable Degree or equivalent experience.

• Software engineering experience on large-scale production systems.

• Experience working successfully with multi-functional teams, principles and architects and coordinate effectively across organizational boundaries and geographies.

• Strong level knowledge of a systems programming language (Go, Python) and a solid understanding of Data Structure and Algorithms.

• High level knowledge of Linux system administration and management.

• Understanding of cluster management systems (Kubernetes, SLURM)

• Understanding of performance, security and reliability in complex distributed systems. Familiarity with system level architecture, data synchronization, fault tolerance and state management.

Ways to stand out from the crowd:

• Proficiency in architecting and managing large-scale distributed systems, independent of cloud providers. Deep knowledge of 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.) Hands-on experience in Machine Learning Operations. Hands-on experience with Bright Cluster Manager.

• Hands-on experience developing and/or operating hardware fleet management systems. Proven operational excellence in designing and maintaining AI infrastructure