Research Scientist, Robot Foundation Model

Wayve Wayve · Robotics · Sunnyvale, CA · Wayve Labs

Research Scientist to build foundation models for general-purpose robots, working across the robot-learning stack from model architectures and learning algorithms to data pipelines, evaluations, and deployment on physical robots. Focus on vision-language-action models, world and action models, multimodal and omni models, video models, reinforcement learning, imitation learning, and behavioral cloning.

What you'd actually do

  1. Research model architectures, data and learning approaches for robot foundation models.
  2. Design, implement and evaluate models such as VLAs, WAMs, omni-modal models, video models and related foundation-model architectures for robotics.
  3. Explore and develop learning approaches including reinforcement learning, behavioural cloning and other methods relevant to robot policy development.
  4. Synthesize, curate and filter large-scale video datasets for model training and evaluation.
  5. Build and use scalable distributed training pipelines and infrastructure for large models and large datasets.

Skills

Required

  • Experience in machine learning, with focus in one or more of: vision-language models, video models, robot policies, foundation models for robotics or embodied AI.
  • Experience with scalable training, such as multi-node training, large datasets and/or large model training.
  • Strong research track record, including publications in top-tier venues such as ICRA, CoRL, CVPR, NeurIPS, ICML or ICLR.
  • Strong coding skills and hands-on experience with modern machine learning frameworks.
  • Ability to design and run rigorous experiments while collaborating closely with engineering and robotics teams.
  • Experience translating research ideas into working systems, experiments or deployed capabilities.
  • Strong communication skills and the ability to share research clearly across teams.

Nice to have

  • PhD or MS in Computer Science, Machine Learning, Robotics, Computer Vision or a related technical field.
  • Industry experience in machine learning, robotics, embodied AI or related applied research environments.
  • Experience with real robots, robotic learning, embodied AI, simulation or policy learning.
  • Experience working with large-scale video data and sequential decision-making systems.

What the JD emphasized

  • foundation models for robotics
  • embodied AI
  • robot policies
  • real robots
  • large-scale video data

Other signals

  • foundation models for general-purpose robots
  • robot learning stack
  • physical robots