Hardware Reliability Engineer

• Reliability analysis & risk assessment: Conduct physics-of-failure modeling for advanced accelerator packaging; assess thermal, mechanical, and electrical stressors; define and execute stress-test protocols including thermal cycling, electromigration, HTOL, HAST/uHAST, and power cycling
• Failure analysis & root cause: Lead failure-mode analysis using C-SAM, X-ray CT, SEM, TEM, FIB, and EBSD; identify cracking, voiding, electromigration, and stress-induced damage; drive corrective/preventive action (8D, FMEA)
• Reliability physics & lifetime prediction: Build and apply models (Coffin-Manson, Arrhenius, Black's equation) and FEA-based stress simulation to predict field lifetime and FIT under real accelerator thermal and power profiles
• OSAT management & collaboration: Partner with assembly and test providers on reliability improvements; define requirements, ensure JEDEC/IPC/IEEE/MIL-STD compliance, monitor OSAT performance, and support supplier audits and qualifications
• System-level reliability: Assess thermal, mechanical, and electrical stress interactions across package, board, and the system the accelerator ships in; drive design-for-reliability into the package and board ↔ package interface with packaging, materials, SI/PI, and thermal
• Cross-functional close-out: Develop design guidelines and reliability best practices, and own the reliability data presented to internal teams and customers
• Fleet & data-center reliability: Translate package- and system-level reliability into fleet availability targets — AFR, FIT, MTBF/MTTR, and availability "nines"; drive detection and mitigation of silent data corruption (SDC) / silent data errors in production; close the loop from field telemetry, returns, and RMA back into design and qual (reliability growth); partner with data-center operations, SRE/hardware-ops, and customers on serviceability and uptime for large-scale training and inference
• Use and develop AI-assisted / ML tool flows to accelerate failure analysis, lifetime modeling, and failure prediction

What we're looking for:

• MS or Ph.D. in Materials Science, Mechanical Engineering, Electrical Engineering, Applied Physics, or related field
• 5+ years in 2.5D/3D advanced packaging reliability
• Deep command of physics-of-failure methodology and strong materials-science knowledge, particularly interconnects and interfaces
• Proficiency in statistical reliability analysis (Weibull, lognormal, acceleration modeling; JMP, Minitab, or Python)