About the Team OpenAI, in partnership with our capital and technology partners, is building a global network of advanced datacenters to support the most demanding AI workloads. The Industrial Compute team ensures that all datacenter systems are manufactured, delivered, and commissioned to the highest standards of quality, reliability, and performance. We work closely with manufacturing partners, engineering teams, and operations staff to ensure that every component is delivered ready for installation, startup, and long-term service. About the Role We are seeking an experienced Quality Engineer (QE) to drive Product and Site Quality initiatives across OpenAI’s infrastructure ecosystem. In this role, you will establish, implement, and manage a comprehensive, quality-focused program across our global supply chain network, ensuring excellence from design through deployment. You will be responsible for end-to-end quality of finished products, as well as maintaining and elevating manufacturing site quality standards. Working cross-functionally with Design (NPI) and Engineering teams, you will help achieve First Pass Yield (FPY), quality, and reliability targets. This includes leading site and fixture validation efforts, driving yield improvement initiatives (Yield Bridge, CPI), and implementing robust corrective and preventive actions (CAPA) to resolve issues at their root cause. In addition, you will play a key role in supplier quality management, assessing and qualifying new vendors, overseeing ongoing supplier performance, and ensuring readiness for future business awards. You will lead vendor audits, monitor key performance metrics, and coordinate corrective actions to ensure predictable delivery schedules, reduced operational risk, and high system reliability. By partnering closely with external suppliers and internal Engineering and Operations stakeholders, you will help ensure OpenAI’s datacenter infrastructure is delivered on time, meets the highest quality standards, and supports reliable, scalable operations. Key Responsibilities - Develop and establish a datacenter-focused manufacturing quality program that supports global deployment schedules. - Conduct vendor capability assessments, quality audits, and improve vendor performance across production, testing, and delivery phases. - Develop and track quality metrics to assess manufacturing performance and identify trends and work to refine processes, training, and quality controls to mitigate deployment risks. - Establish effective cross-functional communication between manufacturing sites, datacenter design teams, and operations. - Collaborate with engineering, sourcing, and site operations to establish quality processes and standards with project goals. - Mentor supplier teams and provide structured feedback to drive continuous improvement. - Audit quality activities at supplier sites to verify compliance with requirements and lead corrective and preventive action plans for audit findings and quality nonconformances. - Facilitate root cause analysis and drive systemic process improvements for recurring manufacturing issues and DC quality and reliability issues. Travel - Domestic and international travel as needed (estimated 30%) to manufacturing sites, datacenter locations, and partner facilities. Qualifications - 10+ years in manufacturing quality management, supplier quality engineering, process improvement, or datacenter engineering/operations. - Bachelor’s degree in engineering, manufacturing, construction management, or related field (or equivalent work experience). - Demonstrated ability to analyze data, solve problems, and improve manufacturing processes. - Proven track record managing deliverables for complex projects or supplier programs. - Strong communication skills with experience influencing both technical and executive audiences. Preferred Skills - Technical knowledge of datacenter infrastructure systems, including mechanical, electrical, and compute components. - Experience in supplier performance management for mission-critical facilities. - ASQ Certified Quality Engineer, Quality Manager, Quality Auditor or Lean Six Sigma certified Green Belt. - 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 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 https://cdn.openai.com/policies/eeo-policy-statement.pdf. 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. 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