About the Position

Principal Machine Learning Engineer, 3D Geometric Vision

Location

Sunnyvale, California, USA

Employment Type

Full-time

Compensation

$370,040 – $419,800 per year

About the Role

We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that.

About Our Teams

The Model Foundations team builds the 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 to develop models that learn 3D structure and dynamics from fleet-scale sensor data.

Responsibilities

As a Principal Engineer on the Model Foundations team, you will be a hands-on technical leader. You'll 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 feedforward 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.

Requirements

Essential

Deep expertise in 3D computer vision, geometric vision, or 3D machine learning, with experience in areas such as 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 a 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++.
A 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: the ability to identify high-leverage problems, set research and engineering direction, make strong architectural decisions, and raise the technical bar across teams.

Desirable

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.

About Wayve

🚀 Wayve is building the leading AI platform for autonomous driving. We are pioneering an end-to-end AI approach that enables vehicles to learn directly from real-world experience, developing the ability to adapt, generalize, and improve at scale. Instead of relying on hand-coded rules or pre-mapped environments, our AI Driver learns to drive by understanding the world around it. The result is technology that navigates complex urban environments with intelligence, precision, and natural flow, unlocking meaningful advances in both safety and efficiency. We believe autonomy represents a once-in-a-generation transformation in how people and goods move, comparable to the shift from horses to cars, and from human-driven vehicles to intelligent machines.

Our ambition is to make autonomy universal. Wayve’s mapless and hardware-agnostic AI platform integrates with global OEM partners, enabling continuous software evolution and unlocking advanced levels of automation from L2 plus through to L4 as our core AI model scales. In a race increasingly defined by intelligence and real-world learning, Wayve is taking a distinct approach, building a generalizable driving intelligence that can power any vehicle, anywhere. By combining embodied AI with scalable deployment, we are creating technology that can be shaped to each OEM brand and driver experience, accelerating the transition to a safer, more intelligent future of mobility.

We Offer

💰 Salaries benchmarked against the market annually

📈 Meaningful equity, sharing in the ownership and long-term success of Wayve

✈️ Relocation support and visa sponsorship where applicable

✅ Hybrid working, core hours, and the chance to work hands-on in vehicle workshops and labs

📚 Learning and development budgets with support for training, conferences, and growth

🩺 Comprehensive benefits including health insurance, dental, enhanced maternity and paternity leave, retirement or pension where applicable, access to therapists, wellbeing partnerships, team socials, and more

The Interview Process

Our process is clear and respectful of your time:

Initial call / recruiter screen [30 mins]
Competency Interviews [hiring manager interview and applied ML interview; 2 hours total]
Deep-dive technical interviews [programming, systems design & technical leadership interviews; 3 hours total]
Final interview: mission & values alignment [Director or VP interview; 45 minutes].

We’ll always explain the format and work around your availability.

A Quick Note

Wayve is not a mature, fully-structured place with the playbook already written. Much of how we work is still being written, and if you join, you’ll help write it. That suits people who want real ownership more than people who need a settled structure from day one.

If that sounds like the kind of problem you want to spend your time on, we’d really like to hear from you.

At Wayve, we're committed to creating a diverse, fair, and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic, or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law.

For more information, visit Careers at Wayve. To learn more about what drives us, visit Values at Wayve.

DISCLAIMER: We will not ask about marriage or pregnancy, care responsibilities, or disabilities in any of our job adverts or interviews. However, we do look to capture information about care responsibilities and disabilities among other diversity information as part of an optional DEI Monitoring form to help us identify areas of improvement in our hiring process and ensure that the process is inclusive and non-discriminatory.

Principal Machine Learning Engineer, 3D Geometric Vision

Job description