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ABOUT THE TEAM
The Compute Infrastructure team runs the GPU fleet and large compute clusters that support OpenAI’s research, ChatGPT, and the API. Our work spans GPUs, CPUs, storage, networking, and cloud infrastructure, bringing these systems together so teams can train and serve models at scale.
We seek to learn from deployment and distribute the benefits of AI, while ensuring that this powerful tool is used responsibly and safely. Safety is more important to us than unfettered growth.
ABOUT THE ROLE
You will own CPU capacity planning and management across Research and Compute. Working with infrastructure owners, you will lead integrated planning for CPU, storage, and network capacity across OpenAI’s global fleet. You will connect workload demand and GPU deployment plans with the foundational resources required to support pretraining, research, and application infrastructure at scale.
You will work closely with workload owners, infrastructure engineering, network deployment teams, Finance, and cloud providers to turn forecasts into supply and allocation plans. You will understand workload architecture, guide decisions when capacity is constrained, and drive opportunities to improve utilization and reduce waste. Your work will span planning over multiple years and ongoing capacity management across regions and providers, including AWS, GCP, Azure, and OCI.
This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.
IN THIS ROLE, YOU WILL:
- Lead CPU demand planning across Research, Compute, and application infrastructure, including foundational services, data acquisition, container platforms, robotics, and services supporting GPU clusters. Maintain forecasts that capture workload growth, regional and hardware requirements, timing, and acceptable alternatives.
- Translate workload growth and GPU fleet plans into integrated CPU, storage, and network requirements. Partner with engineering to determine the capacity, throughput, bandwidth, connectivity, and placement needed to support pretraining and applications at scale.
- Reconcile current and incoming supply with demand, model constrained scenarios, and drive allocation and prioritization decisions. Lead CPU allocation planning and fulfillment, including quota transfers, rebalancing, and reclaims.
- Partner with network engineering and deployment teams to translate regional demand into bandwidth and connectivity plans, ordering inputs, and delivery milestones. Coordinate CPU and network allocations as capacity is deployed across the fleet.
- Understand workload architecture and performance requirements well enough to challenge assumptions and influence technical decisions. Help teams evaluate resource sizing, regional placement, data movement, storage choices, and acceptable alternatives when supply is constrained.
- Drive fleet efficiency programs that reclaim unused capacity, rightsize workloads, modernize infrastructure, and improve placement or resource sharing. Measure the capacity released and cost improvements while preserving workload performance and reliability.
- Scale integrated capacity planning across CPU, storage, and network in partnership with resource owners. Improve demand intake, forecasts, scenario models, inventory and allocation records, and the tools that automate recurring workflows.
- Partner with cloud providers, Finance, Procurement, and engineering to shape capacity orders, commitments, and renewal decisions. Evaluate regional availability, delivery lead times, cost, and technical fit, and drive resolution of supply gaps.
- Align capacity plans with cluster and application deployment milestones. Work across infrastructure owners to ensure CPU, storage, and network dependencies are ready when workloads need them, and resolve gaps that would delay usable capacity.
- Give teams and leadership a clear view of demand, supply, allocation tradeoffs, forecast changes, and risks. Turn ambiguous or competing needs into decisions, accountable owners, and executable plans.
YOU MIGHT THRIVE IN THIS ROLE IF YOU:
- Have 5+ years of experience in technical program management, capacity planning, capacity engineering, or infrastructure management, with ownership of complex programs at scale.
- Have directly managed demand forecasting, supply planning, allocations, and capacity operations for CPU or cloud compute infrastructure. You understand how forecasts, inventory, quotas, delivery schedules, and utilization inform capacity decisions.
- Bring technical depth in one or more infrastructure domains and can reason across the dependencies among CPUs, GPUs, storage, and networks.
- Understand how workload architecture drives CPU and memory needs, storage capacity and throughput, network bandwidth and latency, and regional placement. You can use that knowledge to influence engineering decisions.
- Have worked across multiple cloud providers and understand how provider capabilities, quotas, regional constraints, and commercial commitments affect capacity plans.
- Have built or improved capacity planning tools, data models, or automation. You are comfortable working directly with data to validate assumptions, model scenarios, and measure outcomes.
- Have delivered measurable improvements in infrastructure utilization or cost through reclaims, rightsizing, modernization, workload placement, or other efficiency programs.
- Can lead decisions across engineering, research, product, Finance, and external providers, balancing technical requirements, delivery risk, and cost. You communicate clearly with both working teams and leadership.
- Are resourceful in ambiguous environments, take ownership of outcomes, and build simple processes that continue to work as the fleet grows.
- Are interested in and thoughtful about the impacts of AGI.
About OpenAI
OpenAI is an AI research and deployment company dedicated to ensuring that general-