Senior Applied Scientist for Embodied AI

4 weeks ago

London, Greater London, United Kingdom Wayve Full time

About the Role


We are seeking a Senior Applied Scientist to join our London team, where we are pushing the boundaries of Embodied AI and developing cutting-edge technologies for autonomous systems.


In this role, you will be instrumental in designing and optimizing ultra-efficient foundation models tailored for the unique demands of embodied AI and autonomous systems.


As a Senior Applied Scientist, you will work 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.


Key Responsibilities:



  • Design and optimize ultra-efficient foundation models specifically tailored for autonomous systems and embodied AI.
  • Develop and refine techniques such as model-free and model-based reinforcement learning, and efficient vision-language models to improve the adaptability, reliability, and efficiency of autonomous systems.
  • Collaborate with world-class researchers and engineers to push the boundaries of AI, contributing significantly to the evolution of autonomous driving technology.
  • Influence the future of autonomous vehicles, helping to shape a smarter, safer, and more efficient transportation ecosystem.

About You


You are an applied scientist with a deep understanding of AI/ML and a specific focus on TinyML. You thrive in environments where innovation meets real-world application, and you are driven by the desire to see your research make a tangible impact on autonomous technologies.


Requirements:



  • 5+ 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.

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