ashby
Staff Machine Learning Scientist/Engineer
Wayve Labs
Sunnyvale, USA
Full-time
Senior
Hybrid
USD 370000-419000 yearly / year
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The role
We are looking for a Research Scientist to join the Multi-Embodiment Generalist Agent (MEGA) team within Wayve Science as a founding member.
MEGA is building foundation models for general-purpose robots: models that learn from large-scale video, language, and robot-interaction data, then generalize across tasks and embodiments—including mobile manipulators, dual-arm platforms, and humanoids. Our aim is to build agents that can perceive, reason about, and act reliably in the physical world.
You will help define and build the foundation-model learning stack for robotics: novel model architectures, pre-training objectives, post-training methods, and scalable data and training systems. The work combines frontier ML research with a direct route to real-world evaluation on a growing fleet of robots.
Your work may span vision-language-action models, world and action models, video and multimodal models, imitation learning, reinforcement learning, and self-supervised learning. You will work with large-scale video and robotics datasets and distributed training infrastructure to develop increasingly capable, robust, and general robot policies.
You will collaborate with research scientists, ML engineers, roboticists, and hardware teams to turn promising ideas into large-scale experiments, strong research contributions, and compelling robot demonstrations. This is an opportunity to take meaningful ownership of a new ML-first research program at the frontier of foundation models and embodied intelligence.
Key responsibilities
- Research and develop model architectures, learning objectives, and data strategies for robot foundation models.
- Empirical research experience – experience hill climbing on ML models.
- Experience with various data sources – annotation, filtering, mixing strategies.
- Develop scalable self-supervised and generative pre-training methods using web video, egocentric video, and robot-interaction data.
- Develop post-training approaches—including supervised fine-tuning, imitation learning, reinforcement learning, and related methods—to improve real-world robot capabilities.
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