Design, develop, and productionize robust battery state estimation algorithms for SOC, SOH, and SOP using physicsbased, modelbased, and hybrid datadriven estimation techniques across multiple battery chemistries. Develop observers and filters (e.g., equivalent circuit and electrochemical informed models, Kalman filter based approaches) that remain accurate across temperature extremes, power transients, sensor noise, and battery aging. Incorporate calendar and cycle aging effects into estimation logic so outputs remain truthful throughout the battery lifecycle and across chemistries and pack architectures. Implement estimation algorithms as production quality embedded software in C/C++, meeting GM standards for safety, cybersecurity, and coding discipline, including MISRA compliance. 10% domestic travel required. Hybrid Work Policy - 3 days In-office, 2 days remote - Must be able to report to local office.