Vehicle Performance Modelling Engineer, Robotics

Company: wayve
Apply for the Vehicle Performance Modelling Engineer, Robotics
Location: London
Job Description:

Overview

In this role you will develop physics-based and data-driven virtual vehicle models and validate them against real-world data. You will turn models into robust software components and run experiments to improve accuracy, with impact across simulation, robotics, and product teams. You will help build the go-to tool for simulation and evaluation at Wayve and contribute to cross-functional modelling initiatives. This role offers ownership, collaboration, and the chance to shape autonomous driving capabilities at scale.

Pay / Benefits

  • Salaries benchmarked against the market annually
  • Meaningful equity
  • Relocation support and visa sponsorship
  • Hybrid working with core hours
  • Learning and development budgets
  • Comprehensive health and wellbeing benefits

Responsibilities

  • Develop virtual vehicle models using physics-based and data-driven techniques
  • Validate models against on-road data and improve correlation
  • Convert models into production-quality software components meeting accuracy, performance, and scalability
  • Plan, run, and analyse experimental tests at the track or with external partners
  • Share results with stakeholders across robotics, simulation, and product teams
  • Advance integration of models within Wayve’s simulation, hardware-in-the-loop, and correlation pipelines
  • Engage in cross-functional vehicle characterisation with robotics, simulation, and product teams

Key requirements

  • Experience developing mathematical models and simulation algorithms for vehicle dynamics, robotics, or control systems
  • Strong grasp of classical mechanics, numerical analysis, and computational methods
  • Experience validating simulation models against experimental data
  • Production-quality coding in C++ or Python
  • Experience in vehicle dynamics for autonomous vehicles, automotive, or motorsport
  • Familiarity with data-driven approaches (ML or system identification) for modelling unknown parameters
  • Knowledge of high-fidelity vehicle dynamics tools (Dymola, CarSim, CarMaker) and integrating them into simulations
  • Comfort with cross-functional collaboration
  • Cross-functional collaboration
  • Strong communication of technical results
  • Problem-solving mindset
  • C++
  • Python
  • mathematical modelling

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Posted: October 22nd, 2026