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VÆRIDION

Electric microliner for regional air travel

Senior Battery Algorithms Engineer

Electrical EngineeringConfirmed live in the last 24 hours
Full-timeIn-person

About the Job

Requirements

  • 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, including Kalman filtering (EKF/UKF/dual-UKF/SKF), particle filters, observers, and system modeling, with awareness of data-driven and machine-learning methods and their certification constraints.
  • Solid battery domain knowledge, including 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/C++, fixed-point implementation, Git, and CI/CD.
  • Experience with real-time simulation, HiL testing, and embedded systems.
  • Familiarity with aerospace functional safety and certification, including DO-178C/ED-12C and the DAL concept, requirements-based verification and validation, MBSE and model-based design, and relevant battery and energy standards (ED-289, ED-309, DO-311A, SAE AIR6897).
  • Analytical mindset, strong attention to detail, ability to own deliverables end-to-end, and clear technical documentation skills.

Responsibilities

  • Own 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) using 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 or machine-learning methods while respecting certification limitations of machine-learning-based approaches.
  • Develop mission-aware predictors that use flight-profile and navigation data to predict SoF through safe landing, compute energy margin, predicted surplus SoF at destination, and the SoF trend presented to the crew, and update predictions for diversion, altitude, speed, and ambient changes.
  • Develop aging and degradation models and Remaining Useful Life prediction, and validate the SoH algorithm against applicable end-of-life criteria across representative mission and durability profiles.
  • Ensure 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 algorithms meet functional-safety expectations for a DAL B function, including 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 real-time embedded realization of estimators and predictors on pack, lane, and central control units using fixed-point implementation, Simulink code generation, and 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.

Desired Qualifications

  • DO-160 environmental awareness.

About the Company

VÆRIDION

Electric microliner for regional air travel

Vaeridion develops electric regional air mobility using a zero-emission microliner designed to serve underserved regional corridors. The aircraft seats up to nine passengers plus crew and flies up to 500 km on a single charge, using a glider-inspired wing design integrated with batteries to minimize energy use. The service connects regional airfields that are within about 20 km for 80% of Germany’s population, enabling faster, cleaner travel compared with trains or long road trips. Revenue comes from offering these efficient flights to travelers who otherwise face time-consuming options, with a focus on business travelers who can avoid overnight stays and save time. Vaeridion’s approach differentiates itself by prioritizing energy efficiency and regional accessibility through a purpose-built, eco-friendly aircraft and a network strategy that links smaller airports to reduce travel time.

Company size
51-200
Company stage
Late Stage VC
Total funding
$155.9M
Headquarters
Munich, Germany
Founded
2021

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