Tokens-as-a-service (taas) Lead

OpenAI OpenAI · AI Frontier · San Francisco, CA · Scaling

This role leads the conversion of industrial-scale infrastructure investments into usable token capacity for OpenAI workloads, focusing on the intersection of infrastructure delivery, hardware systems, and operational execution to support AI training and inference.

What you'd actually do

  1. Lead Tokens-as-a-Service programs across industrial compute environments, including first-party and partner-owned capacity.
  2. Convert delivered power, space, and hardware capacity into production-ready token throughput.
  3. Build integrated execution plans spanning construction, power energization, rack deployment, networking, cluster readiness, and workload onboarding.
  4. Partner with infrastructure engineering, hardware, networking, finance, supply chain, and operations teams.
  5. Drive external providers, EPCs, OEMs, utilities, and strategic partners toward aggressive timelines.

Skills

Required

  • hyperscale infrastructure, data center, manufacturing, or industrial deployment programs
  • power + hardware + networking + software combine into usable compute
  • operate equally well with engineers, operators, executives, and external partners
  • highly structured in ambiguous, fast-moving environments
  • escalate intelligently and maintain momentum under pressure
  • translate technical execution into business outcomes

Nice to have

  • GPU clusters, AI infrastructure, hyperscale data centers, or industrial technology programs
  • program management, operations leadership, capacity delivery, or strategic infrastructure execution
  • utilities, construction, commissioning, networking, hardware deployment, or cluster bring-up
  • managing multi-billion-dollar infrastructure or capacity programs
  • Strong executive communication and stakeholder management skills

What the JD emphasized

  • industrial-scale infrastructure investments
  • usable token capacity
  • raw infrastructure capacity must be transformed into operational GPU throughput
  • physical infrastructure delivery with compute utilization outcomes
  • AI training and inference workloads