TinyML Engineer, Autonomous Systems

2 months ago

London, Greater London, United Kingdom Wayve Full time

About Wayve

Wayve is a leading developer of Embodied AI technology, committed to creating a diverse and inclusive culture that values innovation and collaboration.

Our Vision

We aim to create autonomy that propels the world forward, enabling vehicles to perceive, understand, and navigate complex environments with ease.

The Role

We are seeking a Senior Applied Scientist to join our London team, where you will be instrumental in designing and optimizing ultra-efficient foundation models tailored for autonomous systems and embodied AI.

Key Responsibilities

  • Design and optimize foundation models for autonomous systems and embodied AI.
  • Develop and refine techniques such as model-free and model-based reinforcement learning, and efficient vision-language models.
  • Collaborate with world-class researchers and engineers to push the boundaries of AI and contribute to the evolution of autonomous driving technology.
  • Influence the future of autonomous vehicles and 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.

Desirable

  • Track record of publications at top-tier conferences like NeurIPS, CVPR, ICRA, ICLR, CoRL etc.
  • Strong software engineering experience in Python and other relevant languages (e.g., C++ and CUDA).
  • Experience bringing an ML research concept through to production and at scale.

What We Offer

We offer 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 operate core working hours so you can determine the schedule that works best for you and your team.

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