Principal BMS AI Algorithm Developer (Embedded Edge AI) - Automotive - Battery Management System

Principal BMS AI Algorithm Developer (Embedded Edge AI) - Automotive - Battery Management System / Artificial Intelligence

Our client, a leading automotive technology company, is seeking a Principal BMS AI Algorithm Developer to lead the design and deployment of advanced diagnostic and prognostic algorithms for next-generation Battery Management Systems.

This is a rare opportunity to shape the technical roadmap for edge-based intelligence in automotive BMS, working at the intersection of battery cell chemistry, impedance diagnostics and embedded AI.

What you will be doing

• Leading the design and development of AI-driven diagnostic and prognostic algorithms for embedded BMS platforms
• Architecting hybrid models combining battery cell chemistry, impedance diagnostics and AI/ML approaches
• Developing real-time algorithms for State of Charge (SoC), State of Health (SoH), State of Power (SoP), fault detection and safety prediction (including thermal runaway precursors)
• Leveraging electrochemical impedance spectroscopy (EIS) for advanced diagnostics
• Developing and validating algorithms using MATLAB, Simulink and Python
• Deploying and optimising models on embedded platforms (C/C++, AUTOSAR)
• Utilising NXP eIQ AI/ML tools and embedded SDKs for deployment on automotive microcontrollers
• Applying edge AI optimisation techniques including quantization, pruning and efficient inference
• Ensuring compliance with ISO 26262 and automotive OEM standards
• Collaborating across system, hardware, software and validation teams
• Defining the technical roadmap for AI-driven BMS systems and acting as SME for battery algorithms, impedance diagnostics and embedded AI
• Mentoring cross-functional teams

What you will need

• Master's or PhD in Electrical Engineering, Electrochemistry, Computer Science or a related field
• 10+ years' experience in BMS or battery systems, ideally within an automotive OEM or Tier-1 environment
• Deep expertise in battery cell chemistry and electrochemical behaviour
• Proven experience in SoC/SoH/SoP estimation, degradation modelling, and fault diagnostics & safety prediction
• Hands-on experience with MATLAB, Simulink, Python and electrochemical impedance spectroscopy (EIS)
• Experience deploying algorithms on embedded systems (C/C++, AUTOSAR)
• Hands-on experience with the NXP eIQ Machine Learning Software Development Environment and deployment on NXP S32K/S32G platforms or similar automotive MCUs
• Expertise in state estimation and mathematical modelling techniques
• Strong understanding of real-time and resource-constrained systems

Battery cell chemistry & electrochemical modelling, electrochemical impedance spectroscopy (EIS), MATLAB, Simulink, Python, embedded AI/edge ML, eIQ AI tools & automotive MCU platforms (S32K/S32G), AI frameworks (TensorFlow, PyTorch), real-time systems & optimisation, safety-critical automotive systems.

By applying to this role you understand that we may collect your personal data and store and process it on our systems. For more information please see our Privacy Notice (