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Bachelor
Coach, mentor, and scale a Reliability Engineering team across planning, validation, and fleet failure analysis, optimizing resource allocation to navigate evolving data center complexities at a fast-moving pace.
Oversee manufacturing stability to ensure intrinsic product reliability across all verticals at APAC contract manufacturer locations, proactively identifying workflow opportunities to better support dynamic business needs.
Drive Design for Reliability (DfR) methodologies and DFMEAs from the initial concept phase, formalizing a lessons learned pipeline to directly shape design rules for next-generation ML hardware.
Lead high-priority investigations for complex, intermittent field reliability failures, guiding internal teams, OEMs, and external laboratories through advanced failure analysis techniques to validate conclusions and enforce strict remediation standards.
Utilize statistical tools, physics-of-failure models, and internal reliability data to predict product life performance, feedback application stress, enable early detection, and define comprehensive end-of-life strategies.
Minimum qualifications:
Bachelor's degree in Electrical Engineering, Mechanical Engineering, Reliability Engineering, Materials Science, or a related technical discipline, or equivalent practical experience.
10 years of experience in manufacturing.
8 years of experience in people management.
Preferred qualifications:
Experience with large-scale data center infrastructure, high-density compute/server topologies, or power/cooling sub-systems.
Demonstrated experience in performing risk mitigation during early design phases using predictive modeling or reliability simulations before design lockdown.
Experience designing and executing accelerated life testing (ALT, HALT) and manufacturing detection profiles tailored to data center environmental profiles.
Deep expertise in structured problem-solving methodologies (e.g., 8D, FMEA, FTA) and physical failure analysis for complex electronic assemblies or server-grade hardware.
Strong background in data analysis tools (e.g., JMP, SQL, Python/R) for life-data analysis, Weibull modeling, and predicting fleet-wide failure rates.