SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Wayve is building the leading AI platform for autonomous driving using an end-to-end AI approach that enables vehicles to learn directly from real-world experience. The company is pioneering mapless, hardware-agnostic technology that adapts and generalizes at scale.
The Model Foundations team builds geometric vision and 3D foundation models that underpin Wayve's autonomous driving systems, working at the intersection of large-scale deep learning, geometric computer vision, and real-world robotics.
As a Principal Engineer on the Model Foundations team, you will be a hands-on technical leader setting direction for geometric vision research and engineering. Your responsibilities include:
- 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 producing high-quality geometric supervision from large sensor data volumes
- Develop and scale offline SLAM and 3D reconstruction systems to recover accurate trajectories, scene geometry, calibration signals, and geometric supervision
- Apply techniques in multi-view geometry, neural rendering, NeRFs, Gaussian Splatting, implicit 3D representations, and feedforward 3D modeling
- Explore geometry-aware tokenization and representation learning for efficient encoding and fusion across cameras, viewpoints, time, and sensing modalities
- Develop foundation vision models leveraging camera, radar, LiDAR, and other sensor data for rich physical-world representations
- 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 with rapid iteration 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 collaborate across foundation models, perception, simulation, data, sensing, and deployment teams
Wayve operates a hybrid working model combining in-person collaboration in dedicated office spaces with focused remote work. Main hubs include London, Sunnyvale, Yokohama, Herzliya, Vancouver, and Leonberg.
REQUIREMENTS:
Essential:
- Deep expertise in 3D computer vision, geometric vision, or 3D machine learning, with experience in multi-view geometry, neural rendering, reconstruction, implicit representations, or world modeling
- Strong experience designing, training, and evaluating modern deep-learning models at scale using PyTorch or comparable framework
- Strong mathematical and technical foundations in geometry, linear algebra, probability, optimization, and 3D transformations, combined with excellent software engineering skills in Python and C++
- Track record of taking difficult research problems from idea to working system, including building large-scale data, training, evaluation, or deployment pipelines
- Principal-level technical leadership: ability to identify high-leverage problems, set research and engineering direction, make strong architectural decisions, and raise technical bar across teams
Desirable:
- Experience with 3D and geometric vision: multi-view geometry, dense 3D reconstruction, neural fields, NeRFs, Gaussian Splatting, or feedforward 3D models
- Foundation and world models: large-scale vision pre-training, self-supervised learning, video models, generative models, or learned scene dynamics
- Geometric data engines: offline SLAM, structure-from-motion, reconstruction, calibration, auto-labeling, and large-scale ground-truth generation
- Multimodal perception: learned representations and fusion across camera, radar, LiDAR, and other sensing modalities
- Production ML systems: distributed training, large-scale experimentation, and deploying neural networks on real-time, resource-constrained hardware
