Key Notes
Autonomous-driving investigations across hardware, software and models require at least three years with complex robotics, AV or comparable engineered systems. The London role uses Python, experiment design, simulation and fleet data to diagnose recurring issues and on-road incidents. Wayve states hybrid working with core hours; desirable evidence includes machine-learning evaluation, safety-critical systems and automotive, robotics or AI experience.
What You'll Work On
- Triage on-road data and investigate recurring fleet issues, tracing root causes across robot hardware, models and software.
- Analyse investigation data and communicate patterns through dashboards or visualisations that technical and non-technical teams can act upon.
- Partner with engineering, model-development, operations and safety teams, including vehicle rides, to resolve complex full-stack system issues.
- Develop automated investigation workflows and diagnostic playbooks for recurring issues and test preparation.
- Reproduce faults with simulation and offline tools while supporting incident response, emerging programmes and release testing.
Why This Role Matters
- On-road incidents and recurring fleet issues span Wayve’s robot, model and software layers; full-stack root-cause analysis directs corrective work to the responsible system owners.
- Simulation and automated investigation playbooks reproduce faults away from the fleet, allowing hypotheses and fixes to be tested with fewer vehicle resources.
What They Are Looking For
- Experience: At least three years working with complex robotics, autonomous vehicles or comparable engineered systems is essential.
- Technical: Python for analytical scripting, including use of AI tools to work more efficiently, is essential.
- Technical: Investigation across hardware, software and models, plus experience designing real-world experiments, is essential.
- Stakeholder: Explaining complex findings to technical and non-technical audiences and influencing direction through findings are essential.
- Preferred: Machine-learning evaluation, safety-critical systems, automotive, robotics, AI or startup experience, and statistical or experimental best practices, are desirable.