Vehicle Validation Engineer

Wayve Wayve · Robotics · London, United Kingdom +1 · Product & Delivery

Wayve is seeking a Vehicle Validation Engineer to ensure the real-world deployment of their AI driving systems. The role involves planning and executing vehicle tests, developing validation concepts, ensuring vehicle readiness, delivering test data and insights, and providing feedback on system performance from both technical and customer perspectives. The engineer will also contribute to improving testing processes and operational best practices as the team scales, and manage external supplier relationships. The role requires experience with vehicle diagnostics tools, structured vehicle testing, and the ability to analyze test data. Experience with autonomous vehicle systems and scripting/data tools is desirable.

What you'd actually do

  1. Plan and execute vehicle tests to support development, validation, benchmarking, and real-world deployment.
  2. Develop and improve vehicle validation test concepts to evaluate system performance across a range of real-world scenarios.
  3. Work closely with engineering, product, and operations teams to translate testing needs into effective test activities.
  4. Ensure vehicles are prepared, maintained, and consistently ready for safe and efficient testing.
  5. Deliver high-quality test data, performance reporting, and actionable insights to support rapid iteration and decision-making.

Skills

Required

  • Experience with CANalyzer, CANoe, or similar tools for vehicle diagnostics, along with vehicle instrumentation and data logging tools (e.g. Vector, ETAS, Racelogic).
  • Experience executing structured vehicle tests in proving ground and/or real-world environments.
  • Comfortable working across Linux, Windows, and SSH-based environments.
  • Ability to interpret technical requirements and translate them into clear test cases and validation plans.
  • Strong communication skills, with the ability to work effectively across engineering, product, and operations teams.
  • Proactive, hands-on approach with a focus on improving processes, tools, and ways of working.
  • Understanding of safe testing practices and risk assessments in safety-critical environments.
  • Ability to analyse test data and deliver clear, actionable insights to engineering teams.

Nice to have

  • Experience working with autonomous vehicle or ADAS systems.
  • Familiarity with scripting or data tools (e.g. Python, MATLAB) for analysis or automation.
  • Understanding of vehicle networks (CAN, LIN, Ethernet) and diagnostic protocols.
  • Exposure to simulation or test environments (e.g. HiL, SiL).
  • Awareness of automotive safety standards or regulatory frameworks (e.g. ISO 26262).

What the JD emphasized

  • end-to-end AI driving systems
  • real-world deployment
  • test activities
  • system performance
  • real-world scenarios
  • test data
  • performance reporting
  • vehicle behaviour
  • driving performance
  • system gaps
  • customer perspective
  • benchmarking
  • testing processes
  • operational best practices
  • autonomous vehicle
  • ADAS systems

Other signals

  • AI driving systems
  • foundation models
  • automated driving systems
  • vehicle validation
  • test activities
  • system performance
  • real-world scenarios
  • test data
  • performance reporting
  • vehicle behaviour
  • driving performance
  • system gaps
  • customer perspective
  • benchmarking
  • testing processes
  • operational best practices
  • autonomous vehicle
  • ADAS systems