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Reliability Engineer, Amazon Leo Satellite Engineering

AmazonSeattle, WAL6 LeadSeptember 15th, 2026
Overview In this role you will lead reliability for Amazon Leo hardware, building a center of excellence in reliability analysis and test. You will collaborate with subsystem teams to guide designs, perform stress-driven risk analysis, and develop system-level models for spacecraft optimization. You drive vendor qualification and critical component selection, aiming to meet rigorous reliability targets. This is a hands-on, impact-focused role shaping the resilience of the satellite constellation. Compensation / Benefitshealth insurance401(k) matchingpaid time offparenteral leaveRSUssign-on payments ResponsibilitiesBuild a center of excellence for reliability analysis and test for Kuiper hardware designDevelop system-level reliability models and allocate reliability targets across subsystemsReview and guide FMECAs, predictive modelling techniques, and test plansEstablish test capabilities and failure analysis as neededInterface with subsystem teams to input on designs and support stress analysis and risk assessmentsDrive critical component identification, vendor qualification, and quality/reliability metrics Key requirementsBachelor's degree or above in Mechanical Engineering, Materials Engineering, Reliability Engineering, Electrical Engineering, or related field3+ years of hardware reliability engineering experience in satellite systems or space applicationsKnowledge of developing functional specs, design verification plans, and functional test proceduresKnowledge of reliability engineering principles, methods, and toolsKnowledge of environmental testingProficiency in failure analysis techniques and materials characterizationFamiliarity with electronics (PCBs, PCBAs, electronics components) and moving mechanical assembliescross-functional collaborationanalytical mindsetstrong communicationPhysics-of-Failure based reliability approachesReliability modeling (RBD, Markov models, statistical modeling, data analytics)FMECAs, predictive modelling techniques