Data Center Hardware Quality & Reliability Engineer

OpenAI; United States; Remote; USD 150,000 - 210,000; Full time

Job summary

OpenAI seeks a reliability-focused engineer to own the end-to-end hardware quality loop for data-center infrastructure. You will turn field failures into quantified risk, drive fast containment, and ensure verified root cause analysis to improve manufacturing-test coverage and spare forecasting.

The role requires blending practical hardware/system understanding with reliability engineering and data fluency, while leading cross-functional technical teams at data-center scale.

Qualifications

• Hardware and systems understanding relevant to data-center scale.
• Experience driving reliability through metrics and data.
• Ability to lead cross-functional teams in technical settings.

Responsibilities

• Build and govern the field-quality data model across telemetry and related data.
• Define AFR, ASR, DPPM, MTBF/MTTR, repeat-repair, NTF and forecast accuracy with denominators and uncertainty.
• Provide fleet-level and micro views to detect shifts and bound populations.
• Lead systemic field-failure triage, containment, failure analysis, and CAPA processes.
• Develop life-data, reliability-growth, and spare-demand projections by product and geography.
• Collaborate with MQE and NPI to translate field mechanisms into manufacturing-test coverage and controls.
• Verify upstream changes reduce field recurrence and improve field performance.
• Define FA standards, data contracts, scorecards, escalation paths, and closure evidence.
• Provide inputs on serviceability, TCO, and spares without owning inventory or procurement.
• Create executive decision packages with risk, options, and recommendations.

Skills

Hardware understanding

Reliability engineering

Data fluency

Cross-functional leadership

Education

Bachelor's degree in Engineering or related field

Tools

SQL

Python

Excel

Job description

About The Role Own the end-to-end data-center hardware quality and reliability loop for OpenAI's 3P infrastructure and 1P current and next-gen platforms. Turn field failures into quantified risk, fast containment, verified root cause, improved MQE/NPI and manufacturing-test coverage, accurate spares forecasts, and upstream changes that prevent recurrence. The first hire must combine practical hardware/system understanding, reliability engineering, data fluency, and cross-functional technical leadership at data-center scale.

Key Responsibilities

• Build and govern the field-quality data model across telemetry, tickets, RMA/repair, FA, firmware, configuration, supplier, and manufacturing genealogy.
• Define AFR, ASR, DPPM, MTBF/MTTR, repeat-repair, NTF, repair-cycle-time, and forecast-versus-actual metrics with explicit denominators and uncertainty.
• Provide fleet-level macro views and unit/FRU/cohort-level micro views; detect shifts and bound affected populations.
• Lead systemic field-failure triage, containment, failure analysis, 8D/CAPA, risk assessment, corrective-action verification, and recurrence monitoring.
• Develop cohort, life-data, reliability-growth, and spare-demand projections by product, FRU, supplier, configuration, geography, and age.
• Partner with MQE and NPI to convert field mechanisms into manufacturing-test coverage, screening/stress profiles, diagnostics, control plans, DFR/DFS requirements, FMEA/FTA, mission profiles, FRU strategy, and qualification gates.
• Close the loop by verifying whether upstream changes reduce field recurrence.
• Define supplier/CM FA standards, field-data contracts, scorecards, escalation paths, and closure evidence.
• Provide serviceability, TCO, and spares inputs without owning inventory execution or procurement.
• Create concise executive decision packages: population at risk, exposure, confidence, options, cost/risk, and recommendation.
• Run the cross-functional reliability council and, as the team grows, mentor