Senior Engineer – Automotive Prognostics and Health Management

Responsibilities

• Develop scalable, robust health management solutions across vehicle platforms, influencing design and serviceability from early-stage concept to deployment.
• Collaborate with engineering, software, data science, service, and operations teams to embed PHM features into vehicle systems and digital infrastructure.
• Develop, validate, and deploy advanced fault detection, diagnostic, and prognostic algorithms using statistical and machine learning methods, ensuring scalability, efficiency, and reliability of the models.
• Drive the development of health assessment tools and maintenance prioritization frameworks that improve uptime and optimize service resource planning.
• Establish monitoring pipelines and metrics to assess health model performance post-deployment and identify opportunities for refinement.
• Guide PHM system integration, leveraging inputs from FMEA/FMEDA, diagnostics, and field performance data.
• Stay at the forefront of PHM technology, Software-Defined Vehicle architectures, and data-driven reliability practices. Apply emerging trends to advance our tools, methodologies, and product capabilities.

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.
• One Team Mentality: Must be a self-starter, capable of independently identifying and pursuing opportunities to advance the team’s vision. Must regularly seek and incorporate feedback to ensure alignment with the team’s goal.

Preferred Qualifications

• Familiarity with automotive standards, vehicle dynamics, and telematics data.
• Exposure to 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.