Technical Program Manager, Capacity Analytics & Tooling

The Compute Infrastructure team runs the GPU fleet and large-scale compute clusters that serve the models backing ChatGPT and the API, while also supporting training workloads for our next generation models. We operate a large, modern GPU fleet and provide a unified platform for other OpenAI teams to run production Applied AI and Research training workloads.

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.

You will join the Compute Infrastructure TPM team as a Technical Program Manager for Capacity Analytics & Tooling. You will serve as the product manager for our supply and demand planning tools, owning the strategy, roadmap, and delivery of the systems teams use to forecast demand, understand supply, and plan allocations. Your users will include Product TPMs, Capacity TPMs, and Compute TPMs, alongside partners across Finance, Accounting, Leadership, Engineering, and Research.

This role combines product management, technical program leadership, data engineering, and applied data science. You will partner with engineering and data teams to build reliable data foundations, improve forecasting and scenario planning, and scale our tools and success metrics across CPU, storage, network, and GPU resources. You will help teams make informed capacity decisions across AWS, GCP, Azure, OCI, and other infrastructure providers.

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.

• Own the product strategy and roadmap for supply and demand planning tools. Understand user needs, set priorities, define success measures, and guide delivery from initial requirements through adoption and ongoing improvement.

• Build useful planning workflows. Translate complex processes into tools for demand intake, forecasting, supply tracking, scenario analysis, and allocation planning. Help teams understand what infrastructure they need, when it will be available, and which gaps require action.

• Scale the data infrastructure behind capacity planning. Partner with engineering on ingestion, transformation, storage, and delivery of planning data. Guide technical tradeoffs as the number of resources, providers, data sources, and users grows.

• Make planning data consistent and trustworthy. Establish shared data models and definitions for supply, demand, allocations, utilization, and cost. Drive data cleaning, validation, reconciliation, and traceability so users can understand and trust planning outputs.

• Advance forecasting and analytical capabilities. Work with engineering and data partners to connect product and research plans to infrastructure demand. Make assumptions, uncertainty, forecast accuracy, and capacity constraints visible to decision makers.

• Define and scale meaningful success metrics. Measure data quality, forecast performance, planning speed, and tool adoption. Establish consistent ways to track capacity availability, utilization, and efficiency across CPU, storage, network, and GPU resources.

• Support planning across multiple cloud providers. Build capabilities that account for differences in resource types, regions, availability, commercial commitments, and deployment constraints across AWS, GCP, Azure, OCI, and other providers.

• Lead delivery across data, infrastructure, and planning teams. Manage dependencies, technical tradeoffs, migrations, risks, and releases. Keep teams aligned on priorities and the decisions needed to move work forward.

• Drive adoption and reduce manual planning work. Develop simple workflows, documentation, training, and feedback mechanisms. Identify recurring reconciliation and reporting work that can be improved through better data and automation.

• Make capacity tradeoffs clear to leadership and partner teams. Provide useful views of gaps between supply and demand, allocation options, and their financial and operational consequences. Partner with Finance and Accounting to align planning data with budgeting and reporting needs.

• Have 5+ years of experience leading technical programs or products in data platforms, infrastructure, capacity planning, or a related field, with a record of delivering systems that teams rely on.

• Have owned an internal product or platform roadmap and translated complex operational needs into requirements, priorities, and releases that users adopt.

• Have direct experience with capacity planning, including demand forecasting, supply planning, allocations, and resource constraints. You understand how utilization, performance, availability, and cost affect planning decisions.

• Understand modern data infrastructure, including ingestion, transformation, batch and streaming processing, and analytical storage. You have worked with technologies such as Fivetran, dbt, Spark, Kafka, or comparable systems and can discuss their architectural tradeoffs.

• Have practical experience with data modeling, data cleaning, validation, and reconciliation. You can use SQL and Python or similar tools to investigate discrepancies, validate assumptions, and prototype analyses.

• Have applied quantitative methods to forecasting, scenario analysis, or optimization. You can assess model assumptions and uncertainty, evaluate forecast performance, and explain the limitations of an analysis.

• Understand the capacity, performance, and cost characteristics of compute, storage, networking, and accelerators. You have experience working across multiple cloud environments and can account for differences between providers.

• Can lead complex programs with many technical dependencies and align teams with different priorities. You communicate technical tradeoffs clearly to engineers, researchers, finance partners, and leaders.

• Are resourceful in ambiguous environments, exercise sound product judgment, and build simple, scalable ways to solve complex planning problems.

• 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-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. 

We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.

For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement.

Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.

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