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Based in Attleborough, Norfolk, Virtuosi Racing has built a long list of accomplishments since entering the GP2/F2 Series before later branching out into the British F4 series. We are looking for someone with a strong interest in developing vehicle simulation tools to expand our engineering team with a new simulation & math modelling role. In this role you will be part of the team responsible for creating, developing, and maintaining vehicle models and simulation tools that are used to enhance our car performance.


  • Own and develop the F2 and F4 vehicle physics models.

  • Calibration of model parameters from real-world data.

  • Develop correlation, data analysis tools and optimisation methods.

  • Collaborate with trackside engineers to apply the tools and identify desired improvements to models and processes.

    If you wish to apply for a role, please follow the information and send your CV and cover letter, stating which role you are applying for, to





  • Minimum Bachelor’s degree in a relevant engineering or numerate discipline.

  • Demonstrable experience developing mathematical models and implementing models in a simulation environment.

  • Experience in multiple programming languages (Python, C, Fortran etc.).

  • Strong physical understanding of mechanics and vehicle dynamics.

  • Industry experience in a comparable role.


  • Postgraduate qualification in a relevant discipline.

  • Expertise in applied mathematics, ideally in several of the following areas: mathematical modelling, optimisation, parallelisation, iterative solvers, non-linear state estimation, or statistics.

  • Experience working with real-time hardware-in-loop simulation systems (ideally including the creation, build and execution of models).


We are looking for someone who has:

  • A passion for delivering excellence with a high attention to detail.

  • A desire to continually expand your professional knowledge and development.

  • A proactive attitude to problem solving and development.

  • A pragmatic and distinct scientific approach.

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