Technical Specialist, Battery Algorithms and Digital Twin, Energy

At Ford Energy, you have the backing of an industrial manufacturing powerhouse with the agility of a dedicated energy startup offering industry leading technology. We offer a competitive compensation package including performance-based bonuses, Ford vehicle discounts, and the opportunity to shape the energy strategy of one of the world's most iconic brands. Learn more at

In this position... 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.

• 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.

• 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.

• 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…