PHM Engineer, Automotive Prognostics and Health Management

Role Summary

Become a Part of Our Fleet Health Management Team

Our Fleet Health Management team is dedicated to reducing vehicle downtime, improving reliability and performance, and optimizing engineering and design. We support operations and maintenance efforts by leveraging predictive analytics to monitor vehicle and fleet health.

Responsibilities

• Develop advanced PHM solutions that eliminate unplanned downtime, optimize maintenance strategies, and drive system-wide improvements across the fleet.
• Own the design, deployment, and performance monitoring of health models for Software-Defined Vehicles (SDVs), working closely with cross-functional teams to integrate PHM strategies into vehicle platforms and service operations.
• Lead technical initiatives, apply reliability principles combined with data-driven methodologies, and shape the future of health management capabilities that improve vehicle reliability, availability, and customer satisfaction.

Qualifications

• Bachelor’s or Master’s degree in Electrical, Mechanical Engineering, Computer Science, or related fields.
• 5+ years of experience in PHM, reliability engineering, diagnostics, or related areas.
• Proficiency in Python, SQL, and Git with strong data analysis and statistical modeling skills.
• Hands-on experience with machine learning for predictive maintenance or reliability forecasting.
• Experience developing and deploying ML models in edge or cloud environments.
• Deep understanding of system reliability principles and failure analysis techniques.
• Strong verbal and written communication skills for documenting and reporting to leadership.

Preferred Qualifications

• Familiarity with automotive standards, vehicle dynamics, and telematics data.
• Experience with Machine Learning Operations (MLOps) and scalable deployment practices.
• Experience with Bayesian networks or probabilistic modeling.
• Experience with uncertainty quantification techniques, such as Bayesian Inference, Monte Carlo simulation.
• Experience with building Digital Twin using physics-based modeling.
• Knowledge of automotive diagnostics, FMEA/FMEDA methodologies, and embedded system design.
• Experience with C++ and software integration for real-time systems.
• Experience with requirements management tools such as JAMA and Caemo.
• Familiarity with Software-Defined Vehicle architectures and service operations integration.