Staff / Senior Machine Learning Engineer, AV Core

Job description

About us Founded in 2017, Wayve is the leading developer of Embodied AI technology. Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems. Our vision is to create autonomy that propels the world forward. Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving. In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future. At Wayve, your contributions matter. We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact. Make Wayve the experience that defines your career! The role As a Senior/Staff Machine Learning Engineer in Wayve's AV Core organization, you will lead the technical direction and delivery of learned emergency manoeuvre prediction and collision detection models. Emergency manoeuvres are rare, high-consequence events that place unusual demands on data, modelling, and validation. You will take the programme from problem definition through modelling, evaluation, integration, and evidence for deployment. The Core Model Safety team builds foundational capabilities for assisted and automated driving - collision avoidance, model understanding, and robustness under failure. You will work in a focused, high-impact senior team with strong ownership, access to large-scale training and fleet data, and close partners in research, simulation, evaluation, and applied engineering. Key responsibilities Drive Core Model Safety roadmap themes owning the full lifecycle from research to offline/online experiments to technology transfer. Train and deploy end-to-end AV 2.0 models for emergency manoeuvre prediction and collision detection on our global fleet, using large-scale, diverse data to validate capabilities and improve generalisation across vehicles, markets, and driving conditions. Collaborate on online occupancy models for geometric and semantic perception. Build high-value open-loop and closed-loop evaluations for core capabilities and representati…