Staff Robotics 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! As a Staff Robotics Engineer in Wayve's AV Core organisation, you will lead the technical direction and delivery of fault detection and fallback systems for driverless operation. You will work alongside machine learning experts to build robust systems, and do some ML yourself. Physical AI is not just a learning problem; deployment in the real-world requires deep systems understanding of robotics. 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 Set the technical strategy and roadmap for fault detection and fallback, from a robotics systems perspective, including its behavioral scope, operating envelope, system interfaces, and measurable acceptance criteria. Design and train fault detection mechanisms using the methods best supported by evidence to enable robust driverless operation. Collaborate across functions and expertise areas with machine learning, inference optimisation, software engineers, etc. Lead integration into the shared driving stack, align technical decisions across teams, and raise the bar through architecture reviews, mentoring, a…