Principal Machine Learning Engineer, Geometric Vision

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 Principal Engineer on the Model Foundations team you will build the geometric vision and 3D foundation models that underpin our autonomous driving systems.You will work at the intersection of large-scale deep learning, geometric computer vision, and real-world robotics, developing models that learn 3D structure and dynamics from fleet-scale sensor data. You will be a hands-on technical leader. You will set direction for geometric vision, prototype and train new model architectures, build the data and supervision needed to scale them, and take successful ideas through to deployment on real vehicles. Key responsibilities Design and train 3D foundation models and world models using large-scale driving data. Develop model architectures for 3D perception, geometric reasoning, reconstruction, and world modeling across space and time. Build scalable data generation and auto-labeling pipelines that produce high-quality geometric supervision from large volumes of sensor data. Develop and scale offline SLAM and 3D reconstruction systems and pipelines, using large-scale sensor data to recover accurate trajectories, scene geometry, calibration signals, and geometric supervision for model training and evaluation. Develop and apply techniques in multi-view geometry, neural rendering, NeRFs, Gaussian Splatting, implicit 3D representations, and feedforward 3D modeling. Explore geometry-…