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About the role: Own the reliability of the advanced packages and systems that turn our AI accelerator silicon into products that survive years in the field. You'll define how we qualify 2.5D/3D and heterogeneously-integrated packages, model their physics of failure, drive root-cause when things fail, and build the reliability engineering that lets us predict lifetime under real workloads. You'll sit at the seam between silicon/packaging and the systems our accelerators run in, partner closely with our OSATs, and own the answer to "will this hold up in the field — and for how long? What you'll do 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) Hands-on failure analysis with C-SAM, X-ray CT, SEM, TEM, FIB, and EBSD Proven track record driving OSAT/partner improvements and managing qualifications Familiarity with JEDEC, IPC, IEEE, and MIL-STD standards Heterogeneous integration, fan-out packaging, chiplet architectures, HBM, or silicon-photonics packaging Electrical reliability mechanisms (electromigration, dielectric/TDDB breakdown) Design-for-reliability (DFR), prognostics, and health management for electronic systems AI-driven reliability modeling or machine learning for failure prediction High-power / high-current package reliability for accelerators or GPUs; customer-facing qualification experience Compensation Final offers depend on level, location, and skills relevant to the role. Additional compensation: equity gr