Senior Datacenter Technical Program Manager, At-scale AI Clusters

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA +2 · Remote

This role focuses on the technical program management of large-scale AI supercomputing systems and GPU clusters within datacenters. The TPM will oversee the lifecycle from design and integration to production support, collaborating with hardware and software teams to build and deploy these systems, and develop reference architectures for customers. Responsibilities include leading integration efforts, coordinating datacenter builds, and communicating with leadership to address critical issues.

What you'd actually do

  1. Collaborate with outstanding engineers and architects to build and deploy large scale GPU computing systems based on NVIDIA's reference supercomputing architectures
  2. Lead the integration of new AI clusters with datacenter facilities with demanding requirements on power, cooling, and instrumentation
  3. Coordinate design and fit-out of new datacenter builds, working with both internal engineering teams and external contractors
  4. Own and produce detailed documentation for the end-to-end process for datacenter fit-out and integration
  5. Communicate internally with engineering leadership to prioritize and address key issues essential to the success of our largest customers

Skills

Required

  • BS in Applied Science or Engineering (or equivalent experience)
  • 8+ years of overall experience
  • Experience with high-performance computing systems and GPU clusters deployed in on-premises datacenters
  • Strong teamwork and interpersonal skills

Nice to have

  • Understanding of datacenter design, including familiarity with power and cooling technologies
  • Expertise in system monitoring and instrumentation of large clusters, using technologies such as Prometheus, Grafana, Splunk, Modbus, and BACNet
  • Experience working with the engineering or academic research community supporting high-performance computing or deep learning

What the JD emphasized

  • datacenter integration
  • AI supercomputing systems
  • GPU clusters
  • power, cooling, and instrumentation