Principal Machine Learning Engineer, 3D Geometric Vision
$370k - $420k • Sunnyvale, California USA • Full-time
Posted 25d ago
Job Location
Sunnyvale, California USA
Tech Stack
Remote Work Policy
On-site
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
Wayve is building the leading AI platform for autonomous driving, pioneering an end-to-end AI approach that enables vehicles to learn directly from real-world experience. This role is on the Model Foundations team, which builds the geometric vision and 3D foundation models that underpin Wayve's autonomous driving systems. You will work at the intersection of large-scale deep learning, geometric computer vision, and real-world robotics to develop models that learn 3D structure and dynamics from fleet-scale sensor data. As a Principal Engineer, you will be a hands-on technical leader, setting direction for geometric vision, prototyping and training new model architectures, building the data and supervision needed to scale them, and taking successful ideas through to deployment on real vehicles.
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.
- Build scalable data generation and auto-labeling pipelines for geometric supervision.
- Develop and scale offline SLAM and 3D reconstruction systems using sensor data.
- Apply techniques in multi-view geometry, neural rendering, NeRFs, Gaussian Splatting, and implicit 3D representations.
- Explore geometry-aware tokenization and representation learning across various data modalities.
- Develop foundation vision models utilizing camera, radar, LiDAR, and other sensor data.
- Explore video and generative modeling for learning scene structure and dynamics.
- Train and evaluate models at scale on distributed compute, iterating on architectures, objectives, and data.
- Develop automated evaluation systems for geometric consistency and 3D understanding.
- Optimize and deploy models into production autonomous-driving systems.
- Set technical direction for geometric vision and collaborate with cross-functional teams.
Requirements
- Deep expertise in 3D computer vision, geometric vision, or 3D machine learning (e.g., multi-view geometry, neural rendering, reconstruction, implicit representations, world modeling).
- Strong experience designing, training, and evaluating modern deep-learning models at scale using PyTorch or a comparable framework.
- Strong mathematical and technical foundations in geometry, linear algebra, probability, optimization, and 3D transformations.
- Excellent software engineering skills in Python and C++.
- Track record of taking 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 the technical bar across teams.
- Experience with 3D and geometric vision techniques like multi-view geometry, dense 3D reconstruction, neural fields, NeRFs, Gaussian Splatting, or feedforward 3D models.
- Experience with foundation and world models, including large-scale vision pre-training, self-supervised learning, video models, generative models, or learned scene dynamics.
- Experience with geometric data engines such as offline SLAM, structure-from-motion, reconstruction, calibration, auto-labeling, and large-scale ground-truth generation.
- Experience with multimodal perception, including learned representations and fusion across camera, radar, LiDAR, and other sensing modalities.
- Experience with production ML systems, including distributed training, large-scale experimentation, and deploying neural networks on real-time, resource-constrained hardware.