Vehicle Modelling Engineer

The role

As a Vehicle Performance Modelling Engineer you will be responsible for developing vehicle dynamics models and ensure simulations accurately represent real-world performance. This role provides the opportunity to implement the mathematical models, integrate them into the software stack, and evaluate their accuracy through simulation and experimental testing.

This role offers brings a unique opportunity to shape the future of vehicle dynamics at Wayve by creating accurate representations of vehicles for both online and offline systems. You will work cross-functionally with robotics, simulation and product teams to integrate vehicle models into Wayve’s innovative end to end driving stack.

• Vehicle Modelling:
  Develop virtual vehicle models using physics-based and data-driven techniques.

• Mathematical and Physical Modelling:
  Create detailed models that represent vehicle and subsystems virtually.

• Validation and Performance Correlation:
  Validate models with on-road data and identify and implement correlation improvements.

• Model Integration and Optimisation:
  Convert models into efficient, high-quality software components that meet accuracy, performance and scalability requirements.

• Performance Analysis:
  Own processes, tools and experiments to evaluate on-road vehicle performance metrics.

• Experimental Work:
  Plan, execute and analyse experimental tests to improve models accuracy.

• Communication and collaboration:
  Share results and insights with stakeholders to foster collaboration and drive innovation.

In order to set you up for success as a Vehicle Performance Modelling Engineer at Wayve, we’re looking for the following skills and experience.

• Experience in developing mathematical models and simulation algorithms for vehicle dynamics, robotics or control systems.

• Understanding of classical mechanics, numerical analysis and computational methods.

• Experience in validating simulation models against experimental data.

• Software development with C++ or Python.

• Previous experience in vehicle dynamics modelling for autonomous vehicles, automotive or motor sport.

• Use of data-driven approaches (e.g. machine learning or system identification) for modelling systems with unknown physical parameters.

• Familiarity with high-fidelity vehicle dynamics modelling tools (e.g. Dymola, Car Sim, Car Maker) and integration in simulation workflows.