Over the past few years, we’ve been developing virtual sensor technologies that focus on replacing or complementing physical sensors in automotive chassis systems, particularly in the areas of tires and suspension. By leveraging signal processing, data-driven modeling, and basic AI techniques, we help automotive companies reduce hardware dependency while enabling new functions such as vehicle condition monitoring and load estimation.

With the increasing shift toward Software Defined Vehicles and broader access to vehicle data, exciting opportunities have emerged in chassis modeling and advanced analytics. We're looking for a Mid-Level Data & Simulation Engineer who is eager to work with vehicle dynamics data—especially from tires and suspension systems—and contribute to the development of the next generation of virtual sensor solutions.

• Process and analyze large-scale time-series data from vehicle sensors using advanced signal processing techniques (time and frequency domain).
• Develop data-driven simulation models to estimate vehicle component loads (e.g., tires, suspension, chassis elements).
• Implement and optimize numerical models for vehicle dynamics applications.
• Work with Python and/or Matlab/Simulink for data analysis, simulation, and model validation.
• Collaborate with cross-functional teams, including data scientists and software engineers, to integrate simulation models into automotive software solutions.