Senior Applied Scientist (Neural Reconstruction)

Who are we?

Our team is the first in the world to use autonomous vehicles on public roads using end-to-end deep learning. With our multi-national world-class technical team, we’re building things differently. 

We don’t think it’s scalable to tell an algorithm how to drive through hand-coded rules and expensive HD maps. Instead, we believe that machine learning algorithms learning from experience and data will allow our driver to be more intelligent and capable of easily adapting to new environments.

Our aim is to be the future of self-driving cars: the first to deploy in 100 cities across the world bringing autonomy to everyone, everywhere.

The impact you will have:

We're looking for an experienced Applied Scientist with expertise in Neural Radiance Fields (NeRFs) and Gaussian Splatting to join our Vision & Graphics team and advance our innovative neural simulator, Ghost Gym. This role is central to improving Ghost Gym's capabilities, utilizing state-of-the-art neural rendering techniques to craft photorealistic 4D worlds. You'll be at the forefront of developing and applying groundbreaking research to generate thousands of simulated scenarios. These scenarios are critical for training, testing, and debugging our end-to-end AI driving models, contributing significantly to the creation of safe and reliable AI driving technology. Your work will focus on improving the efficiency, realism, and dynamism of our simulations, especially for dynamic and outdoor environments, pushing the limits of current photorealistic visualization technologies.

Challenges you will own:

Conduct cutting-edge research in NeRFs, Gaussian splatting, and related technologies, with a focus on solving real-world challenges in 3D rendering. Develop and implement algorithms for efficient, high-quality 3D scene reconstruction and rendering, particularly for dynamic and outdoor environments. Collaborate with cross-functional teams to integrate research findings into scalable, production-level solutions. Stay abreast of the latest developments in the field, evaluating and incorporating state-of-the-art techniques into our workflows. This could involve opportunities to publish research findings in top-tier journals and conferences, contributing to the scientific community and establishing Wayve as a leader in the field.

What you will bring:

Must haves:

Proven track record of research in NeRFs, Gaussian splatting, or closely related areas, demonstrated through publications or deployed applications. Strong programming skills in Python, with experience in deep learning frameworks such as PyTorch. Solid foundation in mathematics and physics underlying 3D graphics and rendering techniques. Excellent problem-solving skills and the ability to work independently as well as in a team environment. Demonstrated ability to work collaboratively in a fast-paced, innovative, interdisciplinary team environment.

Desirable:

Experience with dynamic scene reconstruction and rendering, particularly in outdoor environments. Familiarity with parallel computing, GPU programming, and optimization techniques. PhD or MSc in Computer Science, Computer Engineering, or a related field, with a focus on computer graphics, computer vision, or machine learning.

Our offer:

A position to shape the future of autonomous driving, and thus to tackle one of the biggest challenges of our time Immersion in a team of world-class researchers, engineers and entrepreneurs Competitive compensation and stock options On-site chef and bar, lots of fun socials, a workplace nursery scheme and more! Help relocating/travelling to London, with visa sponsorship Flexible working hours - we trust you to do your job well, at times that suit you and your team.

Wayve is built by people from all walks of life. We believe that it is our differences that make us stronger, and our unique perspectives and backgrounds that allow us to build something different. We are proud to be an equal opportunities workplace, where we don’t just embrace diversity but nurture it - so that we all thrive and grow.