Key Notes
CANalyzer, CANoe or similar diagnostic tools, structured proving-ground or real-world vehicle testing, and Linux, Windows and SSH environments are essential. The engineer develops validation concepts, prepares vehicles and turns test data into feedback on Wayve’s end-to-end AI driving systems. This full-time London role follows a hybrid policy and combines test execution with benchmarking, performance reporting and supplier coordination.
What You'll Work On
- Plan and execute vehicle tests supporting development, validation, benchmarking and real-world deployment of Wayve’s end-to-end AI driving systems.
- Develop validation concepts and translate engineering, product and operations requirements into effective test activities covering real-world scenarios.
- Prepare and maintain test vehicles so they remain ready for safe, efficient validation activity.
- Produce test data, performance reports and actionable feedback on vehicle behaviour, driving quality and system gaps.
- Benchmark system performance over time while improving test processes, documentation and supplier or service-partner coordination.
Why This Role Matters
- Wayve’s end-to-end AI driving systems are tested across real-world scenarios; validation plans and prepared vehicles expose behaviour and performance gaps relevant to deployment.
- Engineering, product and operations teams use test data and performance reports to iterate the system; actionable feedback connects observed vehicle behaviour to the next development decisions.
What They Are Looking For
- Technical: Experience with CANalyzer, CANoe or similar vehicle-diagnostic tools, plus instrumentation and data-logging tools such as Vector, ETAS or Racelogic, is essential.
- Experience: Experience executing structured vehicle tests in proving-ground or real-world environments is essential.
- Technical: Working across Linux, Windows and SSH-based environments is essential.
- Technical: Interpreting requirements into test cases and validation plans, then analysing data into actionable engineering insights, is essential.
- Domain: Understanding safe testing practices and risk assessments in safety-critical environments is essential.
- Preferred: Autonomous-vehicle or ADAS experience, Python or MATLAB, CAN/LIN/Ethernet, HiL or SiL, and ISO 26262 awareness are desirable.