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 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 not self-driving vehicles. Our goal is to create 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 rare opportunity to help build a new robotics program from the ground up while working closely with experienced researchers in foundation models, embodied intelligence and large-scale machine learning. You will have meaningful ownership of research projects, contribute to the team’s technical direction and see your ideas tested on real robotic systems. You will work across the robot-learning stack: developing model architectures and learning algorithms, building scalable data and training pipelines, designing evaluations and deploying policies on physical robots. The goal is to produce both strong research and increasingly general, robust and useful real-world capabilities. Your day-to-day work may span vision-language-action models, world and action models, multimodal and omni models, video models, reinforcement learning, imitation learning and behavioral cloning. You will work with large-scale video and robotics datasets, distributed t…