Staff Applied Scientist

Where you will have an impact

We are seeking an  experienced researcher to be a founding member of our Vancouver team! We are prioritising someone with experience leading projects in AI applied to autonomous driving or similar robotics or decision-making domains, inclusive, but not limited to the following specific areas:

• Foundation models for robotics or embodied AI
• Model-free and model-based reinforcement learning
• Offline reinforcement learning
• Large language models
• Planning with learned models, model predictive control and tree search
• Imitation learning, inverse reinforcement learning and causal inference
• Learned agent models: behavioural, oral and physical models of cars, people, and other dynamic agents

Challenges you will own

You’ll be working on some of the world’s hardest problems, and able to attack them in new ways.  You’ll be a technical leader within 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:

• Actively contributing to the Science’s technical leadership community, inclusive of proposing new projects, organising their work, and delivering substantial impact across Wayve.
• Leveraging our large, rich, and diverse sources of real-world driving data
• Architecting our models to best employ the latest advances in foundation models, transformers, world models, etc, evaluating and incorporating state-of-the-art techniques into our workflows
• Investigating learning algorithms to use (e.g. reinforcement learning, behavioural cloning)
• Leveraging simulation for controlled experimental insight, training data augmentation, and re-simulation
• Scaling models efficiently across data, model size, and compute, while maintaining efficient deployment on the car
• Collaborating with cross-functional, international teams to integrate research findings into scalable, production-level solutions
• Potentially contributing to academic publications for top-tier conferences like NeurIPS, CVPR, ICRA, ICLR, CoRL etc. working in a world-class team, contributing to the scientific community and establishing Wayve as a leader in the field

About you 

Essential

• Proven track record of research in one or more of the topics above demonstrated through deployed applications or publications.
• Experience leading a research agenda aligned with larger organisation or company goals
• Strong programming skills in Python, with experience in deep learning frameworks such as PyTorch, numpy, pandas, etc.
• Experience bringing a machine learning research concept through the full ML development cycle
• 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 bringing an ML research concept through to production and at scale
• PhD in Computer Science, Computer Engineering, or a related field

What we offer you

• The chance to be part of a truly mission driven organisation and an opportunity to shape the future of autonomous driving.  Unlike our competitors, Wayve is still relatively small and nimble, giving you the chance to make a huge impact
• Competitive compensation and benefits
• A dynamic and fast-paced work environment in which you will grow every day - learning on the job, from the brightest minds in our space, and with support for more formal learning opportunities too
• A culture that is ego-free, respectful and welcoming
• 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 also operate core working hours so you can be where you need to be for family and loved ones too. Teams determine the routines that work best for them

This is a full-time role based in our office in Vancouver.  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.