GPT Infra Lead: Scale Industrial Compute Capacity

OpenAI; San Francisco (CA); On-site; USD 300,000 - 420,000; Full time

Job summary

OpenAI in San Francisco is seeking a GPT Infrastructure Lead to drive end-to-end conversion of industrial-scale infrastructure investments into usable token capacity for OpenAI workloads.

You will own execution across complex compute programs, coordinating across data center delivery, power, networking, hardware deployment, workload onboarding, and external partners to ensure capacity becomes productive tokens quickly and efficiently.

Qualifications

• Experience leading hyperscale infrastructure or industrial deployment programs.
• Ability to translate technical delivery into business outcomes.
• Strong executive communication and stakeholder management skills.

Responsibilities

• Lead GPT Infrastructure programs across industrial compute environments, including first-party and partner-owned capacity.
• Convert delivered power, space, and hardware capacity into production-ready token throughput.
• Build integrated execution plans spanning construction, power energization, rack deployment, networking, cluster readiness, and workload onboarding.
• Partner with infrastructure engineering, hardware, networking, finance, supply chain, and operations teams.
• Drive external providers, EPCs, OEMs, utilities, and strategic partners toward aggressive timelines.
• Identify bottlenecks between physical delivery and usable compute output, then lead resolution.
• Build operating cadences, dashboards, forecasts, and executive reporting for TaaS programs.

Job description

OpenAI in San Francisco is seeking a GPT Infrastructure Lead to drive end-to-end conversion of industrial-scale infrastructure investments into usable token capacity for OpenAI workloads.

You will own execution across complex compute programs, coordinating across data center delivery, power, networking, hardware deployment, workload onboarding, and external partners to ensure capacity becomes productive tokens quickly and efficiently.