Senior Reliability Engineer

EVgo

58d

About The Position

EVgo (Nasdaq: EVGO) is one of the nation’s largest public fast charging networks for electric vehicles. Our mission is to expedite the mass adoption of electric vehicles (EVs) by creating a convenient, reliable, and affordable EV charging network that delivers fast charging to everyone. EVgo’s owned and operated charging network is growing rapidly. We partner with multiple stakeholders including automakers; fleet and rideshare operators; retail hosts such as grocery stores, shopping centers, restaurants, gas stations, and more to make our vision of Electric for All a reality. The EV industry is one of the fastest growing industries in the country. Join us as we charge forward into an all-electric future. Software is an integral part in realizing this vision and we are hiring a software manager to play a leadership role shaping our future. We are seeking a Senior Reliability Engineer to evaluate, predict, and enhance product reliability through statistical analysis, reliability modeling, and testing. The role involves life data analysis, reliability risk assessment, system-level modeling, and collaboration with cross-functional teams to ensure products meet performance and reliability targets throughout the development lifecycle. The ideal candidate will have a strong background in reliability statistics and Weibull analysis, reliability testing (ALT/HALT/HASS), failure analysis and risk methods (FMEA/FTA), and data analysis using reliability tools.

Requirements

Typically requires a minimum of 5 years of related experience with a Bachelor’s degree; or 3 years and a Master’s degree; or a PhD without experience; or equivalent work experience.
Strong knowledge of reliability engineering principles, life data analysis, and product failure mechanisms.
Experience performing Weibull analysis and applying statistical distributions such as Weibull distribution, exponential, and lognormal.
Experience with reliability risk assessment methods (FMEA, DFMEA, FTA) and executing reliability testing including ALT, HALT, and HASS.
Strong statistical analysis and data interpretation skills with experience analyzing time-to-failure datasets and reliability metrics.
Experience with reliability and statistical software tools such as ReliaSoft Weibull++, Ansys Sherlock, Minitab, and programming/analytical tools such as Python, R, or MATLAB.
Strong analytical, problem-solving, organizational, and communication skills with the ability to present technical findings and reliability insights to cross-functional teams and management.
Strong analytical and problem-solving skills with the ability to interpret complex reliability and statistical data.
Detail-oriented and methodical, able to identify patterns, anomalies, and failure trends in datasets.
Excellent technical communication skills, capable of presenting reliability findings, risks, and recommendations clearly to cross-functional teams.
Collaborative mindset, working effectively with design, quality, manufacturing, and test engineering teams.
Proactive and data-driven, able to identify potential reliability risks early in the product lifecycle and recommend mitigation strategies.
Organized and able to manage multiple reliability assessments, testing activities, and projects simultaneously.
Continuous learner, staying updated on emerging reliability engineering methods, statistical techniques, and analytical tools.

Responsibilities

Perform life data analysis using the Weibull distribution to evaluate product reliability and predict failure behavior.
Analyze time-to-failure data from laboratory testing, accelerated life testing (ALT), HALT, HASS, and field returns to identify reliability trends and failure patterns.
Develop reliability models, perform system-level reliability assessments, and apply reliability growth methodologies to track and improve product performance.
Conduct reliability risk assessments using Failure Mode and Effects Analysis, DFMEA, and Fault Tree Analysis to identify potential failure modes and mitigation strategies.
Lead root cause investigations and failure analysis for anomalies identified during testing or field performance.
Clean, organize, and analyze reliability datasets, generate metrics (MTBF, MTTF, B10 life), and communicate insights to engineering teams and management.
Collaborate with design, quality, and manufacturing teams to improve product reliability throughout the development lifecycle.