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Job Description
Wayve is seeking a Lead Applied Scientist to join their London team. The candidate will be instrumental in designing and optimizing ultra-efficient foundation models tailored for autonomous systems and embodied AI. This role involves working on the cutting edge of AI/ML, contributing to models that are both powerful and resource-efficient, enabling seamless integration into real-world autonomous environments. The Lead Applied Scientist will collaborate with world-class researchers and engineers to push the boundaries of AI, helping to shape a smarter, safer, and more efficient transportation ecosystem.
Role Involves:
- Designing and optimizing ultra-efficient foundation models for autonomous systems and embodied AI.
- Developing and refining techniques such as model-free and model-based reinforcement learning, and efficient vision-language models.
- Collaborating with researchers and engineers to advance autonomous driving technology.
Requirements:
- 7+ years of ML engineering / applied science experience in an industrial research environment
- Experience in GenAI, EfficientAI, LLMs, World Models, Reinforcement Learning, or Autonomous Driving
- Passion for working in a team on research ideas that have real-world impact
- Strong programming skills in Python, with experience in deep learning frameworks such as PyTorch, numpy, pandas, etc.
- Several years of experience working on machine learning algorithms and systems
- A good grasp of machine learning literature
- Comfortable working with large quantities of image and video data
- Good insight into the practical aspects of training, validation, testing, and metrics for deep learning features/models
- MS or PhD Machine Learning, Computer Science, Engineering, or a related technical discipline, or equivalent experience
Wayve offers:
- A hybrid working policy combining time in offices and workshops with remote work.
- Core working hours to allow flexible scheduling.
