Manufacturing Quality Engineer, Data Center Equipment

The posting, as OpenAI wrote it

About the Team

OpenAI and its capital and technology partners are building a global network of data centers for demanding AI workloads. The Infrastructure Quality team sets and verifies quality, reliability, and performance requirements for equipment across that network.

We work with manufacturing partners, general contractors, engineers, and operations staff from supplier qualification through commissioning. Together, we verify that equipment arrives ready for installation, startup, and long-term service.

About the Role

As a Manufacturing Quality Engineer, you will oversee the quality of data center equipment at supplier factories. You will use production data, audits, and test results to identify systemic, recurring, or safety-related issues and work with suppliers to address them through Closed Loop Learning (CLL).

You will work with suppliers and internal teams to define inspections and factory tests for each type of equipment. You will coordinate issue resolution before shipment and manage deviations across multiple suppliers and project sites.

Key Responsibilities

Manufacturing Monitoring and Supplier Improvement

• Manufacturing monitoring: Work with Manufacturing Operations to monitor production progress, quality controls, inspection results, and test readiness. Flag emerging risks and coordinate a response with suppliers.
• Product and process audits: Audit products and processes during and after production against engineering requirements, critical-to-quality (CTQ) characteristics, approved procedures, and quality standards. Document findings and track corrective actions to closure.
• Manufacturing coordination: Agree on production checkpoints and inspection and testing requirements with suppliers, engineering, sourcing, supply chain, commissioning, and quality teams. Escalate risks to equipment acceptance or delivery.
• Manufacturing scorecards: Develop supplier and product scorecards with Manufacturing Operations. Review audit findings, test results, recurring defects, and open issues with suppliers to prioritize improvements.
• Drive continuous improvement: Lead the CLL process with suppliers. Track root cause analyses and corrective and preventive actions in a shared register. Verify that each action addresses the underlying problem before closing it.

Factory Acceptance Testing (FAT)

• Standardize requirements and formats: Develop product-specific FAT requirements and standard test formats with equipment suppliers and OpenAI’s engineering, commissioning, quality, and Manufacturing Operations teams.
• Define critical quality requirements: Document the CTQ characteristics each test must verify in an OpenAI-accepted format. Use CLL findings to revise FAT procedures so future tests check for known failure modes.
• Ensure auditable supplier records: Require suppliers to retain test results and make them available for OpenAI audits.
• Manage documentation and revision control: Review final FAT records and aud

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