{"schemaVersion":"jobsearcher.job.v1","id":"58cecf26b2ac7154cae210d2","url":"https://jobsearcher.com/jobs/58cecf26b2ac7154cae210d2","canonicalUrl":"https://jobsearcher.com/jobs/58cecf26b2ac7154cae210d2","title":"Hardware Reliability Engineer","description":"ITAR Notice: This role involves access to ITAR-controlled information. Applicants must be U.S. persons (U.S. citizens, U.S. permanent residents, asylees, or refugees) per 22 CFR 120.62.\n\nAbout the Role\nOwn 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?\".\n\nWhat You'll Do\n\nReliability 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.\n\nFailure 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).\n\nReliability 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.\n\nOSAT 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.\n\nSystem‑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.\n\nCross‑functional close‑out: Develop design guidelines and reliability best practices, and own the reliability data presented to internal teams and customers.\n\nFleet & 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.\n\nUse and develop AI‑assisted / ML tool flows to accelerate failure analysis, lifetime modeling, and failure prediction.\n\nWhat We're Looking For\n\nMS or Ph.D. in Materials Science, Mechanical Engineering, Electrical Engineering, Applied Physics, or related field.\n\n5+ years in 2.5D/3D advanced packaging reliability.\n\nDeep command of physics‑of‑failure methodology and strong materials‑science knowledge, particularly interconnects and interfaces.\n\nProficiency in statistical reliability analysis (Weibull, lognormal, acceleration modeling; JMP, Minitab, or Python).\n\nHands‑on failure analysis with C‑SAM, X‑ray CT, SEM, TEM, FIB, and EBSD.\n\nProven track record driving OSAT/partner improvements and managing qualifications.\n\nFamiliarity with JEDEC, IPC, IEEE, and MIL‑STD standards.\n\nHeterogeneous integration, fan‑out packaging, chiplet architectures, HBM, or silicon‑photonics packaging.\n\nElectrical reliability mechanisms (electromigration, dielectric/TDDB breakdown).\n\nDesign‑for‑reliability (DFR), prognostics, and health management for electronic systems.\n\nAI‑driven reliability modeling or machine learning for failure prediction.\n\nHigh‑power / high‑current package reliability for accelerators or GPUs; customer‑facing qualification experience.\n\nFull compensation packages are based on candidate experience and relevant certifications.\n\nCalifornia pay range\n$220,000 - $350,000 USD\n\n#J-18808-Ljbffr","company":"Densityai","rawCompany":"densityai","city":"Mountain View","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-07-16T03:44:57.471Z","occupations":[{"code":"17-2061.00","title":"Computer Hardware Engineers","slug":"computer-hardware-engineers"},{"code":"17-2131.00","title":"Materials Engineers","slug":"materials-engineers"},{"code":"17-2112.02","title":"Validation Engineers","slug":"validation-engineers"}],"industries":[{"code":"541715","title":"Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)","slug":"research-and-development-in-the-physical-engineering-and-life-sciences-except-nanotechnology-and-biotechnology"},{"code":"334413","title":"Semiconductor and Related Device Manufacturing","slug":"semiconductor-and-related-device-manufacturing"},{"code":"334111","title":"Electronic Computer Manufacturing","slug":"electronic-computer-manufacturing"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Hardware Reliability Engineer","description":"ITAR Notice: This role involves access to ITAR-controlled information. Applicants must be U.S. persons (U.S. citizens, U.S. permanent residents, asylees, or refugees) per 22 CFR 120.62.\n\nAbout the Role\nOwn 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?\".\n\nWhat You'll Do\n\nReliability 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.\n\nFailure 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).\n\nReliability 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.\n\nOSAT 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.\n\nSystem‑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.\n\nCross‑functional close‑out: Develop design guidelines and reliability best practices, and own the reliability data presented to internal teams and customers.\n\nFleet & 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.\n\nUse and develop AI‑assisted / ML tool flows to accelerate failure analysis, lifetime modeling, and failure prediction.\n\nWhat We're Looking For\n\nMS or Ph.D. in Materials Science, Mechanical Engineering, Electrical Engineering, Applied Physics, or related field.\n\n5+ years in 2.5D/3D advanced packaging reliability.\n\nDeep command of physics‑of‑failure methodology and strong materials‑science knowledge, particularly interconnects and interfaces.\n\nProficiency in statistical reliability analysis (Weibull, lognormal, acceleration modeling; JMP, Minitab, or Python).\n\nHands‑on failure analysis with C‑SAM, X‑ray CT, SEM, TEM, FIB, and EBSD.\n\nProven track record driving OSAT/partner improvements and managing qualifications.\n\nFamiliarity with JEDEC, IPC, IEEE, and MIL‑STD standards.\n\nHeterogeneous integration, fan‑out packaging, chiplet architectures, HBM, or silicon‑photonics packaging.\n\nElectrical reliability mechanisms (electromigration, dielectric/TDDB breakdown).\n\nDesign‑for‑reliability (DFR), prognostics, and health management for electronic systems.\n\nAI‑driven reliability modeling or machine learning for failure prediction.\n\nHigh‑power / high‑current package reliability for accelerators or GPUs; customer‑facing qualification experience.\n\nFull compensation packages are based on candidate experience and relevant certifications.\n\nCalifornia pay range\n$220,000 - $350,000 USD\n\n#J-18808-Ljbffr","datePosted":"2026-07-16T03:44:57.471Z","dateModified":"2026-07-16T03:44:57.471Z","hiringOrganization":{"@type":"Organization","name":"Densityai","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Mountain View","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"58cecf26b2ac7154cae210d2"},"url":"https://jobsearcher.com/jobs/58cecf26b2ac7154cae210d2"}}