Machine Learning Scientist/Engineer

About the role

Before the detail, here's the challenge you'd help us solve.

We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that.

Here’s what this particular role covers.

🛠️ About our Science Teams

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.

🧠 Your day-to-day

- Design and run experiments on model architectures, learning objectives and data strategies for robot foundation models.

- Build scalable pre-training and post-training methods using web video, egocentric video and robot-interaction data.

- Curate, filter, mix and evaluate large robotics datasets, including egocentric, UMI and teleoperated data.

- Develop and use distributed training pipelines for large multimodal models and datasets.

- Partner with ML engineers, roboticists and hardware teams to turn research progress into measurable real-world robot performance.

- Communicate findings through rigorous internal reviews, publications and robot demonstrations.

🧩 What you’ll be working on

- Research and develop model architectures, learning objectives, and data strategies for robot foundation models.

- 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.

- Curate, filter, and evaluate large-scale robotics datasets, including egocentric, UMI, and teleoperated data.