Principal Research Scientist, Robot Foundation Model

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 We are looking for a Principal Research Scientist to join the Multi-Embodiment Generalist Agent (MEGA) team within Wayve Science as a founding member. MEGA is building foundation models for general-purpose robots beyond self-driving vehicles. Our focus is on creating intelligent agents that can perceive, reason, move and manipulate the physical world across diverse embodiments, including mobile manipulators, dual-arm platforms and humanoids. This is a senior, high-impact role with genuine 0→1 ownership . You will help define the research agenda, technical strategy and foundations of a new robotics program, working alongside world-class researchers in foundation models, embodied intelligence and large-scale machine learning. You will have the opportunity to work across the entire robot-learning stack: building scalable data flywheels, developing new model architectures and learning algorithms, training large models, designing rigorous evaluations and deploying policies on real robots. The goal is work on compelling and publishable research, but also to turn it into systems that demonstrate increasingly general, robust and useful behavior in the physical world. Your day-to-day work may span vision-language-action models, world and action models, multimodal and omni models, video generation, reinforcement learning, imitation learning and behavioral cloning. You will work with la…