Summary
✨ AI‑Generated
An automotive technology organization is seeking a principal AI algorithm developer to lead advanced diagnostics and prognostics for embedded battery management systems. You will design hybrid models combining battery chemistry, impedance-based diagnostics, electrochemical models, and machine learning, with algorithms optimized for real-time execution on resource-constrained hardware.
Highlights
Principal-level technical leadership at the intersection of battery science, embedded systems, and AI. The role focuses on safety-critical edge intelligence, real-time battery diagnostics, prognostics, and deployment under demanding compute, memory, latency, and functional-safety constraints.
Description
We are seeking a highly experienced Principal BMS AI Algorithm Developer to lead the design and deployment of advanced diagnostic and prognostic algorithms for next‑generation Battery Management Systems (BMS) within an automotive OEM environment.
This role focuses on edge-based intelligence, where algorithms run directly on embedded BMS hardware, operating under strict constraints on latency, compute, memory, and functional safety.
You will drive innovation at the intersection of battery cell chemistry, electrochemical modeling, impedance-based diagnostics, embedded systems, and AI/ML, enabling real-time monitoring of battery health and prediction of safety-critical events.
Key Responsibilities
Lead the design and development of AI-driven diagnostic and prognostic algorithms for embedded BMS platforms.Architect hybrid models combining battery cell chemistry, impedance diagnostics, and AI/ML approaches.Develop real-time algorithms for:State of Charge (SoC)State of Health (SoH)State of Power (SoP)Fault detection and anomaly diagnosisSafety prediction (e.g., thermal runaway precursors)Leverage electrochemical impedance spectroscopy (EIS) for advanced diagnostics.Develop and validate algorithms using MATLAB, Simulink, and Python.Deploy and optimize models on embedded platforms (C/C++, AUTOSAR).Utilize NXP eIQ AI/ML tools and embedded SDKs for deployment on automotive microcontrollers.Apply edge AI optimization techniques (quantization, pruning, efficient inference).Ensure compliance with ISO 26262 and automotive OEM standards.Collaborate across System, hardware, software, and Validation teams.
Required Qualifications
Master’s or PhD in Electrical Engineering, Electrochemistry, Computer Science, or related field.10+ years of experience in BMS or battery systems (Automotive OEM / Tier-1 preferred).Deep expertise in battery cell chemistry and electrochemical behavior.Proven experience in battery algorithm development:SoC / SoH / SoP estimationDegradation modelingFault diagnostics & safety predictionHands-on experience with:MATLAB, Simulink, PythonElectrochemical Impedance Spectroscopy (EIS)Experience deploying algorithms on embedded systems (C/C++, AUTOSAR).Hands-on experience with NXP AI toolchain, including:eIQ Machine Learning Software Development EnvironmentDeployment on NXP S32K / S32G platforms or similar automotive MCUsExpertise in state estimation and mathematical modeling techniques.Strong understanding of real-time and resource-constrained systems.
Leadership & Principal-Level Expectations
Define technical roadmap for AI-driven BMS systems.Act as SME (Subject Matter Expert) in battery algorithms, impedance diagnostics, and embedded AI.Drive innovation in intelligent BMS features.Mentor cross-functional teams.
Key Skills
Battery cell chemistry & electrochemical modelingElectrochemical impedance spectroscopy (EIS)MATLAB, Simulink, PythonEmbedded AI / Edge MLNXP eIQ AI tools & automotive MCU platforms (S32K/S32G)AI frameworks (TensorFlow, PyTorch, etc..)Real-time systems & optimizationSafety-critical automotive systems
More information about NXP in Germany...
