S. degree in mechanical engineering, aerospace engineering, electrical engineering, materials science, or other related discipline
8+ years of experience in reliability engineering, hardware testing, failure analysis, or related fields in the space, automotive, semiconductor, or other high-reliability industries
S. or PhD in mechanical engineering, electrical engineering, chemical engineering, materials science, or other related discipline
Experience developing and executing life testing methods, including accelerated life testing (ALT) and reliability growth programs
Experience with accelerated failure time (AFT) modeling, including Norris-Landzberg and Arrhenius acceleration models, for translating ground test severity to flight conditions
Experience reconciling multiple ground test sources (QTP, ATP, HALT, HASS) and flight telemetry onto a single reliability model, including distinguishing workmanship/infant-mortality failure modes from wear-out mechanisms
Familiarity with success-run and zero-failure demonstration statistics, and sample-size/DOE methods for reliability test planning
Exposure to degradation-based reliability modeling (Gamma or Wiener process models, accelerated degradation testing) as a complement to binary pass/fail Weibull analysis
Experience with thermal, vibration, and TVAC qualification and acceptance testing approaches
Experience with EEE components (Electrical, Electronic, and Electromechanical), including semiconductor devices and packaging technologies, in high-reliability or space applications
Experience building reliability prediction models (Weibull, Kaplan-Meier, Monte Carlo, reliability block diagrams) and performing system-level analyses (FTA, PRA)
Strong foundation in statistical modeling and data analysis, with experience developing custom models for engineering or production data. Experience with Bayesian hierarchical modeling and MCMC tools (e.g., PyMC, Stan, NumPyro) for reliability parameter estimation is a plus
Proficiency in programming languages (such as Python or C++) for data analysis, statistical modeling, and reliability calculations
Familiarity with derating practices, Design for Excellence (DFX), and requirements flow-down in complex hardware systems
Experience with Physics of Failure (PoF) methodologies, including Sherlock or similar modeling tools, and identification of dominant failure mechanisms in electronic hardware and EEE components
Experience with reliability growth tracking or working toward probability of success / availability targets on vehicles or constellations
Strong cross-functional communication skills and ability to clearly present technical findings to both engineering and leadership teams
Experience in a fast-paced startup or new-space environment with high ownership and broad scope
Familiarity with FRACAS and test-effectiveness tracking processes