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 de