Senior Software Engineer - Low Voltage Prognostics - General Motors (GM)

Key Responsibilities

• Design, develop, and validate prognostic models that predict low‑voltage failures using machine learning techniques in Python.
• Integrate models into embedded systems, ensuring real‑time performance and reliability across vehicle platforms.
• Collaborate with hardware, controls, and diagnostics teams to collect, preprocess, and analyze large datasets from vehicle sensors.
• Implement data‑driven monitoring dashboards and alerts to detect parasitic drains and system health issues.
• Validate solutions through simulation, bench testing, and on‑road trials, iterating to meet production standards.
• Document architecture, algorithms, and test procedures for internal and external stakeholders.

Requirements

• 5+ years of software engineering experience in automotive or embedded systems.
• Strong proficiency in Python and experience with machine learning libraries (scikit‑learn, TensorFlow, PyTorch).
• Hands‑on experience with low‑voltage power systems, battery management, and prognostics.
• Solid understanding of data analysis, feature engineering, and model deployment in constrained environments.
• Excellent communication skills and ability to work cross‑functionally in a fast‑paced environment.