Sr. Mechanical Reliability Engineer, Megapack
Tesla
- Facilitate Design FMEA sessions to detect risks, drive reliable design choices and inform validation testing
- Set and communicate reliability requirements and targets for site, product, subsystem, and components
- Define reliability test plans, design test conditions, set test duration and sample size, based on physics of failure modeling and accelerated life testing principles
- Analyze lab test data and field data to identify reliability trends, forecast reliability estimates utilizing statistical analysis (e.g., Weibull analysis, physics of failure models) and recommend design improvements. Analyze usage and environmental conditions from field to improve requirement setting and testing methods
- Communicate and drive test plans, results, learnings, and recommendations to various stakeholders within the organization
- Closely collaborate with cross-functional teams, including test, reliability data science, design, architecture modeling, service, and quality to identify potential reliability issues and drive the corrective actions
- Research failure mechanisms to build more robust validation plans and influence design choices. Drive reliability assessments in product design reviews and clearly communicate risk at each phase of product development and for released products
- Influence supplier selection for higher reliability and provide clear guidance on reliability requirements and demonstration to suppliers
- Facilitate failure analysis to understand root cause and drive resolution of failures occurring during product development phases
- Provide reliability design guidelines and apply reliability lessons learned to enable continuous improvement. Answer complex questions on fleet usage and behavior to enable proactive health monitoring, grow reliability, and minimize field failures
Bachelor's Degree in Mechanical Engineering or a related field and five years of experience in a Reliability Engineering role or equivalent experience
Understanding of accelerated testing methods, governing equations, and physics of failure for different failure mechanisms
Understanding of applied statistics and reliability statistics (Weibull distribution, Maximum Likelihood Estimation, Bayesian methods, Monte Carlo analysis, etc.)
Understanding of Accelerated Degradation modeling and reliability prediction methods
Knowledge of material degradation mechanisms and their impact on mechanical performance
Experience analyzing test results and interpreting reliability insights
Knowledge of reliability growth techniques, such as Crow-AMSAA and Crow Extended
Knowledge of the ReliaSoft Synthesis Platform, including Weibull++, BlockSim, ALTA, RGA, and xFMEA
Working knowledge of failure analysis techniques such as optical microscopy, SEM (Scanning Electron Microscopy), CSAM (Confocal Scanning Acoustic Microscopy), X-ray, cross-sectioning and EDX
Working knowledge of programming languages, preferably Python, or Matlab