Technical Program Manager, AI Infrastructure Capacity Planning
OpenAI
- Pay
- $257k–$335k
- Workplace
- On-site
- Location
- San Francisco · California · United States
- First seen
- 2 hours ago
- Last seen
- 2 hours ago
- Board
- Ashby
Summary
About the Team OpenAI's Industrial Compute organization builds and operates the infrastructure required to train and serve frontier AI models.
Posting
About the Team OpenAI's Industrial Compute organization builds and operates the infrastructure required to train and serve frontier AI models. The Capacity Planning team connects rapidly changing research and product demand with the compute, networking, storage, power, data center, hardware, and operational resources required to make that demand executable. About the Role We are seeking a Technical Program Manager to build and lead capacity planning across OpenAI's large-scale AI infrastructure. You will translate uncertain workload demand into clear infrastructure requirements, allocation decisions, supply commitments, activation priorities, and long-range capacity strategies. This role sits at the intersection of research, engineering, infrastructure, finance, sourcing, deployment, and operations. You will create the planning models, operating cadences, governance mechanisms, and source-of-truth systems that allow teams to understand what capacity is required, what is available, what is at risk, and what decisions must be made. This is not a finance-only forecasting or reporting role. Success requires technical fluency across the infrastructure stack, strong analytical judgment, and the ability to move consequential decisions forward when requirements, timelines, and supply conditions change quickly. Key Responsibilities - Own capacity-planning processes across near-term workload allocation, quarterly execution, and longer-range infrastructure horizons. - Translate research, training, inference, and product demand into compute, accelerator, cluster, networking, storage, rack, power, and site requirements. - Develop scenarios that make assumptions, confidence levels, constraints, sensitivities, and decision points explicit. - Reconcile requested demand against contracted, delivered, installed, activated, and workload-usable capacity. - Partner with research and engineering teams to understand workload priorities, technical dependencies, utilization patterns, and changing requirements. - Partner with sourcing, finance, hardware, deployment, and operations teams to align supply commitments, activation schedules, costs, and delivery risks. - Support allocation and prioritization decisions when infrastructure is constrained or delivery plans change. - Track infrastructure lead times, critical dependencies, utilization, headroom, forecast accuracy, activation readiness, and capacity risk. - Build dashboards, analytical tools, executive