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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Technical Specialist, Battery Algorithms and Digital Twin, Ford Energy - **Company:** Ford Motor Company - **Location:** Dearborn, MI, United States - **Experience:** Expert - **Salary:** $115,500.0 - $218,100.0 - **Contract:** Permanent contract - **Skills:** C++ (Programming Language), Cloud Computing, Cloud Engineering, Code Generation, Python (Programming Language), MATLAB, Machine Learning, State Estimation, Data Streaming, Test Data, Digital Twin, Model-Driven Development, Data Analytics, Machine Learning Operations, Drift Detection - **Published:** October 8, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=46ad9e6f115e60e9 ## About the Role * Master's degree with 8+ years of experience, or PhD with 5+ years, in Electrical, Mechanical, Chemical, or Controls Engineering, Materials Science, or a related field. * Hands-on experience developing and deploying battery algorithms on production systems (vehicle, stationary storage, or consumer device), meaning code or models that ran on real hardware or in a live fleet, not only in simulation. Includes at least two of: SOC, SOH, state of power, RUL/degradation, thermal estimation, or fault detection. * Working knowledge of equivalent-circuit models and Kalman-family observers, and familiarity with reduced-order electrochemical or semi-empirical aging models. * Solid understanding of Li-ion physics: electrochemistry, thermal behavior, and aging mechanisms (e.g., SEI growth, lithium plating, loss of active material). * Proficiency in Python and MATLAB/Simulink, with working familiarity in C/C++ or another production language. * Experience calibrating and validating models against cycling, characterization, and ideally field data, including quantifying estimation error. * Technical leadership across functions without formal authority, and the ability to present trade-offs clearly to executives and non-specialists. Even better, you may have... * PhD in a battery-relevant discipline, or a publication/patent record in state estimation, aging, or controls. * Algorithms running in a deployed fleet: over-the-air updates, model-drift monitoring, and learning from field returns and warranty data. * Stationary storage and LFP experience, including SOC/SOH estimation on flat OCV curves and long calendar-life behavior. * Model-based design, auto-code generation, and SIL/HIL validation of BMS software. * Machine learning for SOH/RUL or anomaly detection, with judgment on when physics-based, data-driven, or hybrid methods fit best. * Digital twin or time-series data platforms (cloud, streaming, MLOps) and edge-to-cloud architecture. * Knowledge of BESS safety standards and practices (e.g., UL 9540/9540A, IEC 62619, NFPA 855) and thermal-runaway early detection. * Experience linking degradation models to warranty, levelized cost of storage, or dispatch optimization, and familiarity with PCS/EMS interactions and grid-service duty cycles. * Prior experience in an OEM battery organization, or standing up an algorithm team or platform from scratch. ## Description Ford Energy is seeking a Technical Specialist to own the battery intelligence stack for our BESS products: the state estimation, diagnostics, prognostics, and digital twin capabilities that determine how safely, reliably, and profitably a storage system performs over a life of 20+ years. This role is for someone who has already shipped battery algorithms and wants to do it again at the start of a new product line. You will set the technical direction and stay hands-on, building, validating, and deploying algorithms that run on production BMS hardware and in the cloud, from cell to pack to container. Because Ford Energy is new, your decisions on architecture, data, and validation will become the foundation of the platform, not a patch on an existing one. You will work most closely with the BESS Cell, Modeling, BMS, EMS, and Digital Services teams, and draw on Ford's Auto battery organization for proven methods and test data, adapting them to stationary storage, where duty cycle, chemistry, and business case differ. What you will own... * Production-grade state estimation: SOC, SOH, Dynamic OCV, and power/energy capability estimators that meet defined accuracy, robustness, and compute targets across temperature, aging, and partial-cycle duty. * A deployable digital twin architecture: a defined edge (BMS/EMS) and cloud split, with data, model, and interface specifications agreed with BMS, EMS, and Digital Services. * A field-data feedback loop: a working pipeline from operating systems to model recalibration, with drift monitoring and a release process for algorithm updates. * Prognostics the business can use: degradation and Remaining Useful Life (RUL) models with quantified uncertainty that support warranty, performance guarantees, and dispatch decisions. * Early-warning diagnostics: fault and anomaly detection that contributes to the safety case, with false-alarm and missed-detection targets set and measured. * A validation framework: test-to-field correlation, SIL/HIL coverage, and release criteria the whole organization trusts. What you'll do... * Lead battery intelligence development across the BESS architecture, and engage Pack, Container, PCS, DC Block, AC, and Controls teams as program needs require. * Define requirements for monitoring, diagnostics, prognostics, and digital twins, including accuracy, latency, compute, memory, data-rate, and sensor needs, and set the interfaces between the algorithm stack and BMS, EMS, and cloud platforms. * Translate battery physics, degradation mechanisms, and operational requirements into implementable software and control strategies. * Build hybrid (physics-based and data-driven) digital twins at cell, pack, and container levels, working with Battery Modeling to turn electrochemical, thermal, and degradation models into reduced-order forms suited to embedded execution. * Design, develop, and deploy algorithms for: + SOC, including approaches that work with flat-OCV chemistries such as LFP + SOH, capacity and resistance tracking, and state of power + Thermal state estimation + RUL and degradation prediction, covering calendar and cycle aging + Cell imbalance, anomaly, and fault detection, including early indicators of safety events + Link lab testing, validation, and field monitoring into one workflow, and quantify uncertainty with explicit error and confidence targets. + Lead technical reviews, architecture decisions, and deployment strategy; align modeling, hardware, controls, and software organizations around them. + Adapt algorithms, monitoring approaches, and validation methods from Ford's Auto battery teams to BESS needs, and share what you learn back. Mentor engineers across modeling, controls, and software, and help shape the battery intelligence team as it grows. +