Senior GPU and Hpc Infrastructure Engineer - Dgx Cloud

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA +1 · Remote

NVIDIA is seeking a Senior GPU and HPC Infrastructure Engineer to scale its AI infrastructure. The role involves automating GPU asset provisioning, configuration, and lifecycle management, implementing monitoring for reliability and scalability, and working on software for GPU clusters. The engineer will collaborate with other teams to ensure seamless integration from hardware to AI training applications.

What you'd actually do

  1. 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.
  2. 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.
  3. Work on software that manages NVLINK topography across GPU clusters.
  4. Build automated test infrastructure that we use to qualify distributed systems for operation.
  5. Work with engineering teams across NVIDIA to ensure your software integrates seamlessly from the hardware all the way up to the AI training applications.

Skills

Required

  • strong programming background
  • knowledge of datacenter hardware, operations, and networking
  • familiarity with distributed systems
  • excellent communication and planning abilities
  • 10+ years of software engineering experience 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)
  • solid understanding of Data Structure and Algorithms
  • Expert 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

Nice to have

  • High Performance Computing (HPC)
  • GPUs
  • high-performance networking (RDMA, Infiniband, RoCE)
  • out-of-the-box thinkers
  • 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

What the JD emphasized

  • strong programming background
  • knowledge of datacenter hardware, operations, and networking
  • familiarity with distributed systems
  • excellent communication and planning abilities
  • 10+ years of software engineering experience on large-scale production systems
  • Expert level knowledge of a systems programming language (Go, Python)
  • Expert 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
  • Deep knowledge of datacenter operations and GPU hardware
  • Hands-on experience working with RDMA networking
  • Hands-on experience and deep understanding of cluster management systems (Kubernetes, SLURM.)
  • Hands-on experience in Machine Learning Operations
  • Proven operational excellence in designing and maintaining AI infrastructure

Other signals

  • scale up its AI Infrastructure
  • build end-to-end automation of datacenter operations
  • large-scale Machine Learning systems
  • reliability, availability, and scalability of GPU assets
  • AI training applications