Principal BMS AI Algorithm Developer (Embedded Edge AI) (f/m/d)

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 diagnosis

Safety 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 estimation
• Degradation modeling
• Fault diagnostics & safety prediction

Hands-on experience with

MATLAB, Simulink, Python

Electrochemical 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 Environment

Deployment on NXP S32K / S32G platforms or similar automotive MCUs

Expertise 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 modeling
• Electrochemical impedance spectroscopy (EIS)

MATLAB, Simulink, Python

Embedded AI / Edge ML

NXP eIQ AI tools & automotive MCU platforms (S32K/S32G)

AI frameworks (TensorFlow, PyTorch, etc..)

Real-time systems & optimization

Safety-critical automotive systems

More information about NXP in Germany...

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