Manufacturing Quality Engineer, Data Center Equipment

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 audit reports for each piece of equipment and maintain a controlled revision history. - Conduct periodic supplier audits: Check that suppliers follow approved test procedures and that their records accurately reflect the tests performed. Factory Witness Testing (FWT) - Align checklists and stakeholders: Develop standard FWT checklists with OpenAI subject matter experts, installing contractors, and the construction management team. Agree on requirements before testing begins. - Manage documentation and reporting: Collect and review final witness test reports and supporting records from participants. - Before shipment, confirm that each witness-test issue is resolved or covered by a documented deviation. Log systemic, recurring, or safety-related findings in the CLL tracker and verify the effectiveness of corrective actions with the supplier. Deviation Management - Assess short shipments and missing or failed tests in partnership with Manufacturing Operations. - Coordinate supplier support to resolve issues at the project site. - Document each deviation and track the agreed actions through closure. Qualifications - Bachelor’s degree in electrical, mechanical, or industrial engineering. - At least 10 years of data center industry experience spanning quality engineering and project management. - Experience coordinating FAT and FWT with suppliers and resolving equipment quality issues. - Ability to develop test checklists, define CTQs, review test results, and maintain auditable quality records. - Experience monitoring supplier production, auditing products and processes during and after production, and using scorecards to improve supplier and product quality. Experience managing controlled documents and deviations. - Ability to coordinate supplier schedules and plan inspections and tests across multiple pieces of equipment. - Ability to communicate quality requirements and coordinate decisions across suppliers, engineering, commissioning, sourcing, supply chain, manufacturing, operations, quality, and construction teams. - Willingness and ability to travel approximately 40–60% of the time to supplier manufacturing facilities and data center project sites. Preferred Skills - Technical knowledge of data center infrastructure, including mechanical, electrical, and controls systems. - Experience in supplier performance management for mission-critical facilities. - ASQ Certified Quality Engineer, Quality Manager, or Quality Auditor. - PMP or equivalent project management certification. - Experience in industrial or high-volume manufacturing environments. 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 encom