Principal Machine Learning Engineer, Geometric Vision Sunnyvale, California

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 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.

• 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 feed forward 3D modeling.
• Explore geometry-aware tokenization and representation learning, including efficient ways to encode and fuse information across cameras, viewpoints, time, and sensing modalities.
• Develop foundation vision models that make effective use of camera, radar, LiDAR, and other sensor data for learning rich representations of the physical world.
• Explore video and generative modeling approaches for learning scene structure, dynamics, and future evolution from driving data.
• Train and evaluate models at scale on distributed compute, rapidly iterating on architectures, objectives, data, and training recipes.
• Develop automated evaluation and ground‑truth systems for measuring geometric consistency, reconstruction quality, 3D understanding, and downstream driving performance.
• Optimize and deploy models into production autonomous‑driving systems, working across model architecture, inference, and onboard constraints.
• Set technical direction for geometric vision at Wayve and work closely with researchers and engineers across foundation models, perception, simulation, data, sensing, and deployment.

In order to set you up for success as a Principal Machine Learning Engineer, Geometric Vision at Wayve, we’re looking for the following skills and experience.

Essential

• Deep expertise in 3D computer vision, geometric vision, or 3D machine learning, with experience in areas such as multi‑view geometry,…