Product Cost Management Lead - AI Infrastructure

About the Team OpenAI’s Hardware organization is building the infrastructure foundation for frontier AI systems. The Supply Chain organization enables scalable, resilient, and efficient supply chains across custom silicon, systems, networking, memory, storage, and rack-scale infrastructure. Our team connects engineering, supplier strategy, manufacturing, planning, deployment, and finance to make hardware cost, supply, and execution tradeoffs clear from early architecture through production and sustaining operations. About the Role OpenAI is seeking a Product Cost Management Lead to own the official costed bill of materials (BOM), system and cluster cost and price baseline, and platform-to-platform total cost of ownership (TCO) for our 1P AI infrastructure. You will turn component, system, POD, manufacturing, deployment, DC, service, and lifecycle inputs into a trusted view of product economics that informs technical and business decisions, including transition timing. You will partner with Engineering, Strategic Sourcing, Manufacturing-partner management, Finance, Planning, Supply Chain Program Management, Operations Program Management, and infrastructure teams to evaluate engineering / design changes, compare alternatives, identify cost-reduction opportunities, and track realized benefits. This is an individual contributor role with broad cross-functional ownership. The ideal candidate combines hardware cost expertise, rigorous modeling, sound judgment, and the ability to explain complex tradeoffs clearly to engineering, operations, and senior leaders. In this role, you will: Own the official costed BOM and system/pod roll-up by platform, generation, configuration, build phase, volume, and mix. Maintain version control and traceability to quotes, source owners, assumptions, and engineering revisions. Publish and maintain the system/pod cost and price baseline, reconciling component pricing and manufacturing-partner inputs with approved configurations and planning assumptions. Build platform-to-platform TCO comparisons covering component content, non-recurring engineering, tooling, manufacturing labor and overhead, yield, scrap, test, logistics, deployment, housing and site costs, power and cooling, networking, spares, service, warranty, and lifecycle costs. Partner with housing and site teams to integrate their cost inputs into product economics, documenting scope, timing, allocation assumptions, and gaps so platform comparisons are consistent and avoid double counting. Quantify the cost impact of engineering change orders and notices (ECOs/ECNs), architecture, specification, configuration, and qualification changes before approval. Assess recurring and one-time costs, implementation timing, inventory exposure, and expected benefits. Develop a prioritized cost-reduction, value-engineering, and second-source opportunity funnel with Engineering, Strategic Sourcing, and manufacturing-partner management. Evaluate qualification costs, investment, payback, supply improvements, and execution risk. Analyze actual, forecast, quoted, committed, and purchase-price costs against the baseline. Explain material variances, identify root causes, and track recovery actions with accountable owners and dates. Build sensitivity and scenario analyses for supplier and manufacturing-partner options, capacity, volume and mix, yield, configuration, site, deployment timing, and lifecycle tradeoffs. Present clear recommendations with assumptions, confidence ranges, and decision deadlines. Validate savings and cost avoidance with Finance and accountable source owners. Distinguish identified opportunities, approved actions, and realized benefits, including offsets and implementation costs. Establish cost-data definitions, BOM hierarchy, allocation methods, currency and volume assumptions, refresh expectations, and change-control processes. Flag stale, incomplete, or unsupported inputs before they affect decisions. Lead recurring product-cost and TCO reviews. Publish concise cost bridges, opportunity updates, risks, and decision packages while retaining the supporting detail needed for technical and financial review. Build scalable models, reporting, and data workflows that improve baseline completeness, refresh speed, analysis turnaround, and decision quality as the hardware portfolio grows. You will own integrated product economics and the cost-analysis methodology and the cost analysis itself. Strategic Sourcing and manufacturing-partner management retain commercial negotiations; Engineering retains design and qualification authority; DC and site teams own their source inputs; Finance retains budgets, accounting, and financial approvals; Planning owns the demand baseline; SCPM owns material readiness; and OPM owns operational execution. You might thrive in this role if you have: Significant experience in hardware product costing, cost engineering, supply chain finance, manufacturing cost analysis, or a related role supporting complex technology products. Experience building and maintaining multi-level costed BOMs, system cost roll-ups, and lifecycle or TCO models across product development, production ramp, and sustaining operations. Strong understanding of hardware cost drivers, including component pricing, manufacturing labor and overhead, NRE, tooling, yield, scrap, test, logistics, and service costs. Demonstrated ability to evaluate engineering changes, second-source options, and cost-reduction opportunities using comparable scenarios and transparent assumptions. Advanced spreadsheet and quantitative modeling skills, with disciplined data reconciliation, version control, and source traceability. Ability to turn incomplete or changing technical and commercial inputs into defensible analysis, identify uncertainty, and drive input gaps to closure. Strong cross-functional influence and communication skills, including concise leadership recommendations and detailed working sessions with engineering, sourcing, manufacturing, and finance teams. High ownership and sound judgment in a fast-moving environment with evolving requirements and competing priorities. Preferred Qualifications Experience with AI infrastructure, hyperscale data centers, custom silicon, servers, networking, storage, or rack-scale systems. Experience analyzing JDM, CM, or ODM cost structures and open-book manufacturing models. Familiarity with DC and site economics, power and cooling, deployment timing, spares, and service costs in infrastructure TCO. Experience with ERP, MRP, and PLM systems, BOM change control, and business intelligence or automation/AI tools. A background in engineering, finance, supply chain, manufacturing, or a related analytical discipline. What Success Looks Like Teams use a complete, current, and traceable costed BOM and system/pod baseline as the source of truth for product economics. Platform TCO comparisons use consistent scope and assumptions, with inputs validated by their owners. Material engineering and sourcing decisions include timely cost-impact analysis and explicit tradeoffs. Cost variances have clear explanations, accountable owners, and recovery actions; savings and cost avoidance are validated. Leadership receives clear recommendations, and cost-management processes scale with the complexity of the 1P hardware roadmap. 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. To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form . No response will be provided to inquiries unrelated to job posting compliance. 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