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Your mission

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 senior 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.

Your Responsibilities

• Process and analyze large-scale time-series data from vehicle sensors using advanced signal processing techniques (time and frequency domain).
• Develop data-driven and physics-based simulation models to estimate vehicle component wear (especially tire and brake).
• Design, implement and optimize numerical and control-oriented models for vehicle dynamics applications using model-based development approaches.
• Translate simulation and control models into production-ready functions and support their deployment on embedded systems (ECUs), including code generation and integration.
• Work with Python and/or MATLAB/Simulink for data analysis, modeling, auto-code generation, and model validation.
• Manage deployment for customers as well as core development and research.
• Collaborate with cross-functional teams, including data scientists and software engineers, to integrate developed models and functions into automotive software architectures.

Your profile

• 8+ years of experience in physical modelling and system-based approaches for vehicle dynamics, motion control, or similar fields.
• Strong understanding of vehicle dynamics, control systems, and signal processing.
• Hands-on experience in model-based development (e.g. MATLAB/Simulink) and transformation of models into embedded software functions.
• Experience with deployment and integration of functions on embedded systems (ECUs), including code generation workflows.
• Degree in electronics, mechatronics, mechanical engineering, or a related field.
• Experience with MATLAB/Simulink is required; Python or C code is a strong plus.
• Knowledge of machine learning concepts is an advantage. Previous experience in ASPICE development is a plus.