{"id":3556449,"url":"https://alion.io/job/openai-manufacturing-quality-engineer-data-center-equipment","title":"Manufacturing Quality Engineer, Data Center Equipment","company":{"id":2,"name":"OpenAI","domain":"openai.com","url":"https://alion.io/company/openai","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":80,"open_postings":208,"ghost_share":0,"stale_share":0.808,"repost_share":0.01,"time_to_fill_p50_days":51,"computed_at":"2026-11-11T05:49:22Z"}},"role":"Industrial Engineering","role_family":"Industrial Engineering","seniority":"staff","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":165000,"max":290000,"currency":"USD","period":"year","gross":null,"usd_annual":290000},"salary_estimate":null,"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CAPA","optional":false},{"name":"OpenAI","optional":false}],"status":"live","first_seen_at":"2026-10-30T22:23:31Z","employer_posted_date":"2026-10-30","last_verified_at":"2026-11-11T07:41:58Z","board_verified":true,"closed_at":null,"days_open":11,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":11},"description":"About the Team\nOpenAI 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.\nWe 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.\nAbout the Role\nAs 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).\nYou 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.\nKey Responsibilities\nManufacturing Monitoring and Supplier Improvement\nManufacturing 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.\n\nProduct 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.\n\nManufacturing 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.\n\nManufacturing scorecards: Develop supplier and product scorecards with Manufacturing Operations. Review audit findings, test results, recurring defects, and open issues with suppliers to prioritize improvements.\n\nDrive 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.\n\nFactory Acceptance Testing (FAT)\nStandardize 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.\n\nDefine 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.\n\nEnsure auditable supplier records: Require suppliers to retain test results and make them available for OpenAI audits.\n\nManage documentation and revision control: Review final FAT records and audit reports for each piece of equipment and maintain a controlled revision history.\n\nConduct periodic supplier audits: Check that suppliers follow approved test procedures and that their records accurately reflect the tests performed.\n\nFactory Witness Testing (FWT)\nAlign 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.\n\nManage documentation and reporting: Collect and review final witness test reports and supporting records from participants.\n\nBefore 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.\n\nDeviation Management\nAssess short shipments and missing or failed tests in partnership with Manufacturing Operations.\n\nCoordinate supplier support to resolve issues at the project site.\n\nDocument each deviation and track the agreed actions through closure.\n\nQualifications\nBachelor’s degree in electrical, mechanical, or industrial engineering.\n\nAt least 10 years of data center industry experience spanning quality engineering and project management.\n\nExperience coordinating FAT and FWT with suppliers and resolving equipment quality issues.\n\nAbility to develop test checklists, define CTQs, review test results, and maintain auditable quality records.\n\nExperience 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.\n\nAbility to coordinate supplier schedules and plan inspections and tests across multiple pieces of equipment.\n\nAbility to communicate quality requirements and coordinate decisions across suppliers, engineering, commissioning, sourcing, supply chain, manufacturing, operations, quality, and construction teams.\n\nWillingness and ability to travel approximately 40-60% of the time to supplier manufacturing facilities and data center project sites.\n\nPreferred Skills\nTechnical knowledge of data center infrastructure, including mechanical, electrical, and controls systems.\n\nExperience in supplier performance management for mission-critical facilities.\n\nASQ Certified Quality Engineer, Quality Manager, or Quality Auditor.\n\nPMP or equivalent project management certification.\n\nExperience in industrial or high-volume manufacturing environments.\n\nAbout OpenAI\nOpenAI 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.\nWe 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.\nFor additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement.\nBackground 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.\nTo 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.\nWe are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link.\nOpenAI Global Applicant Privacy Policy\nAt OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. 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