Sr Reliability Engineer

About EVgo

EVgo (Nasdaq: EVGO) is one of the nation’s largest public fast charging networks for electric vehicles. Our mission is to build a sustainable business that powers our transportation and protects our planet. As a leader in clean transportation, EVgo sits at the intersection of technology, infrastructure, and innovation.

We are rapidly expanding our national footprint through strategic partnerships with retailers, automakers, rideshare, and fleets, bringing fast charging to communities across the country. EVgo enables drivers of all makes and models to charge where they want, when they want.

At EVgo, sustainability is not just what we do; it is who we are. We are committed to reducing carbon emissions, advancing clean energy solutions, and building infrastructure that supports a more sustainable future. We believe great companies are built by great people. Our culture is mission driven, customer centric, collaborative, and innovative, with a strong focus on creating an environment where employees can grow and make a lasting positive impact.

We are seeking a Senior Reliability Engineer for an onsite position in El Segundo, CA. The role evaluates, predicts, and enhances product reliability through statistical analysis, reliability modeling, and testing. Responsibilities include 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 has 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.

• 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.

• Minimum of 5 years 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 Relia Soft 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.
• Detail‑oriented and methodical, able to identify patterns, anomalies, and failure trends in datasets.
• 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…