Senior Battery Algorithms Engineer (all genders)

Your Mission:

We are developing a next-generation electric aircraft propulsion system, with the propulsion battery designed, developed, and validated in-house. Accurate real-time observation and prediction of the battery’s State of Function (SoF), the energy and power actually accessible for safe flight and landing, is the safety-critical output of the battery system, and the basis for pre-flight planning, in-flight energy management, and the energy information presented to the crew.

SoF is built on the underlying battery states that your algorithms observe and predict: state of charge (SoC), state of health (SoH), state of power (SoP), state of energy (SoE), and state of balance (SoB). Erroneous observation or prediction of SoF is classified as a catastrophic condition, since it can lead the crew to commit to a flight or an approach the battery cannot support. This makes the function certification-critical and places exacting demands on the accuracy, robustness, and verifiability of every algorithm you deliver.

As a Senior Battery Algorithms Engineer (all genders), you will join the VÆRIDION Technology team and take ownership of the development and validation of the state observers, estimators, and predictors that determine SoF for our lithium-ion propulsion battery systems.

This is a hands-on senior role in which you lead the modeling, simulation, algorithm development, test-data analysis, and validation behind these states, and set the methodology the team works to.

Your work will shape the high-voltage battery system at the center of VÆRIDION’s electric propulsion architecture and carry it through to certification.

Your Day-to-Day:

• Own the end-to-end development and validation of battery state observers, estimators, and predictors for SoF, SoC, SoH, SoP, SoE, and SoB across cell, cell-bank, and pack level

• Parameterize multi-RC equivalent-circuit models (OCV, R0, RC pairs, hysteresis, thermal, and aging effects) from cell supplier data, virtual and physical test benches, and HiL rigs

• Design, implement, and validate estimation algorithms such as Kalman filters (EKF/UKF/dual-UKF/SKF), particle filters, observers, and data-driven/ML methods, while respecting the certification limitations of ML-based approaches

• Develop mission-aware predictors that ingest flight-profile and navigation data to predict SoF until safe landing, and compute the energy margin, the predicted surplus SoF at destination, and the SoF trend presented to the crew, updating on diversion, altitude, speed, and ambient changes

• Develop aging and degradation models and Remaining Useful Life (RUL) prediction, and validate the SoH algorithm against the applicable end-of-life criteria across representative mission and durability profiles

• Ensure the algorithms align with ED-289 requirements for state observers and predictors and ED-309 methodologies for energy level information to the crew, and are consistent with the “Manage Energy” functional breakdown

• Ensure the algorithms meet functional-safety expectations for a DAL B function: deterministic and bounded execution time, reproducibility, uncertainty bounds on outputs, plausibility checks, built-in test, and malfunction and fault detection

• Derive, document, and trace algorithm requirements from ED-289/ED-309 and system-level functional analysis, and maintain traceability into the real-time battery management software specification using a model-based design approach

• Support the real-time embedded realization of estimators and predictors on the pack, lane, and central control units (fixed-point implementation, Simulink code generation, MISRA C) and validate them on HiL setups

• Define the test and analysis strategy for virtual and physical battery test benches and HiL rigs, and analyze cell, cell-bank, and pack-level data to evaluate algorithm accuracy, stability, and robustness across temperature, aging, and usage variability

• Set the standard for documentation of methods, assumptions, and validation results

• Lead collaboration with cell engineering, BMS software, test, HV/LV electrical systems, and certification teams

Your Profile:

• Bachelor’s or Master’s degree in Electrical Engineering, Aerospace Engineering, Mechanical Engineering, Applied Mathematics, Control Engineering, or a related field

• 5+ years of professional experience in battery state estimation, control systems, or embedded algorithm development

• Strong command of state estimation and control theory: Kalman filtering (EKF/UKF/dual-UKF/SKF), particle filters, observers, and system modeling, with an awareness of data-driven and ML methods and their certification constraints

• Solid battery domain knowledge: lithium-ion electrochemistry, equivalent-circuit and thermal models, aging and degradation mechanisms (capacity fade, resistance growth, lithium plating), OCV hysteresis, and cell-bank-level state granularity

• Proficiency in Matlab/Simulink and Python, with familiarity with C