Hardware Data and Failure Analysis Engineer

Navigate and synthesize product datasets (telemetry, reliability results, failure analysis (FA), field returns, customer sentiment, etc.) to identify and motivate areas for product improvement.Identify true root causes by leveraging all available data sources, including direct FA, and referring to cross-product and generation-over-generation information. Distill findings into a clear problem statement.Collaborate with cross-functional teams to develop mitigation strategies, and identify pathways to validate these strategies.Recommend and drive corrective action to improve future products.Develop software, AI skills, and processes to automate and accelerate future workstreams.Minimum qualifications:Bachelor's degree in Hardware Engineering or equivalent practical experience.4 years of experience analyzing hardware performance, manufacturing, or lab test data.Experience investigating technical anomalies or field failures, finding root causes, and documenting corrective actions.Experience designing, debugging, or performing failure analysis on hardware.Experience applying statistical methods, data modeling (e.g., regression), or machine learning to solve physical engineering problems.Experience in technical communication, including simplifying data into concise messages.Preferred qualifications:Master's or PhD degree in Electrical Engineering, Computer Engineering, Physics, or a related field.ASQ certification (CRE, CQE, etc.)Experience with AI: utilizing agent harnesses, version control of skills, etc.Experience working at multiple stages in the product life cycle.Knowledge of Python, SQL, JMP, Weibull++ or similar tools for data analysis and visualization.Track record of working across disciplines such as bridging the gap between direct lab environments and data analytics pipelines.As a Reliability Engineer, you will play a key role in creating new consumer electronic products that meet a high bar for reliability and performance. You will work closely with the product management and design engineering teams to define standards, specify tests, and then supervise test execution and failure analysis. A broad engineering background and command of statistical methods will help to inform design of new products. Your strong interpersonal and communication skills will be key to ensuring adoption of your technical recommendations.We are looking for a curious, cross-functional problem solver to help us understand exactly how our products perform in the real world. In this role, you will be the bridge between our labs and the field using both direct forensic analysis mechanical engineering/electrical engineering (ME/EE/Materials) and data analytics to uncover the story behind issues.You won’t just find the root cause; you will collaborate directly with design and manufacturing teams to turn those insights into future-proof solutions. You will grow on cross-functional teamwork to directly shape next-generation products.Google's mission is to organize the world's information and make it universally accessible and useful. Our Devices & Services team combines the best of Google AI, Software, and Hardware to create radically helpful experiences for users. We research, design, and develop new technologies and hardware to make our user's interaction with computing faster, seamless, and more powerful. Whether finding new ways to capture and sense the world around us, advancing form factors, or improving interaction methods, the Devices & Services team is making people's lives better through technology.Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $132000 - $190000 (USD) + 15% bonus target + equity + benefitsLearn more about benefits at Google.Bachelor's degree in Hardware Engineering or equivalent practical experience.4 years of experience analyzing hardware performance, manufacturing, or lab test data.Experience investigating technical anomalies or field failures, finding root causes, and documenting corrective actions.Experience designing, debugging, or performing failure analysis on hardware.Experience applying statistical methods, data modeling (e.g., regression), or machine learning to solve physical engineering problems.Experience in technical communication, including simplifying data into concise messages.