Applied Scientist New London

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. 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.

We are currently looking for people with research expertise in AI applied to autonomous driving or similar robotics or decision-making domains, including but not limited to the following specific areas:

• Model-free and model-based reinforcement learning
• Offline reinforcement learning
• Planning with learned models, model predictive control and tree search
• Imitation learning, inverse reinforcement learning and causal inference
• Learned agent models: behavioural and physical models of cars, people, and other dynamic agents

You’ll be working on some of the world’s hardest problems, and able to attack them in new ways. You’ll be a key member of our diverse, cross-disciplinary team, helping teach our robots how to drive safely and comfortably in complex real-world environments. This encompasses many aspects of research across perception, prediction, planning, and control, including:

• How to leverage our large, rich, and diverse sources of real-world driving data
• How to architect our models to best employ the latest advances in foundation models, transformers, world models, etc.
• Which learning algorithms to use (e.g. reinforcement learning, behavioural cloning)
• How to leverage simulation for controlled experimental insight, training data augmentation, and re-simulation
• How to scale models efficiently across data, model size, and compute, while maintaining efficient deployment on the car

You also have the potential to contribute to academic publications for top-tier conferences like NeurIPS, CVPR, ICRA, ICLR, CoRL etc. working in a world-class team to achieve this.

What you’ll bring to Wayve:

• Thorough knowledge of and 5+ years applied experience in AI research, computer vision, deep learning, reinforcement learning or robotics
• Ability to deliver high quality code and familiarity with deep learning frameworks (Python and Pytorch preferred)
• Experience leading a research agenda aligned with larger goals
• Industrial and/or academic experience in deep learning, software engineering, automotive or robotics
• Experience working with training data, metrics, visualisation tools, and in-depth analysis of results
• Ability to understand, author and critique cutting-edge research papers
• Familiarity with code-reviewing, C++, Linux, Git is a plus
• PhD in a relevant area and/or track records of delivering value through machine learning are a big plus.

What we offer you:

• Attractive compensation with salary and equity
• Immersion in a team of world-class researchers, engineers and entrepreneurs
• A unique position to shape the future of autonomy and tackle the biggest challenge of our time
• Bespoke learning and development opportunities
• Relocation support with visa sponsorship
• Flexible working hours - we trust you to do your job well, at times that suit you and your time
• Benefits such as an onsite chef, workplace nursery scheme, private health insurance, therapy, daily yoga, onsite bar, large social budgets, unlimited L&D requests, enhanced parental leave, and more!

This is a full-time role based in our office in London. At Wayve, we want the best of all worlds so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, and time spent working from home. We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. If you’re passionate about self-driving cars and think you have what it takes to make a positive impact on the world, we encourage you to apply.