Staff Machine Learning Scientist/Engineer

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.

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

• Build and use distributed training pipelines for large models and large multimodal datasets.

• Work closely with robotics and hardware teams to connect model progress to measurable real-world performance.

• Communicate research clearly internally and, where appropriate, through publications and Wayve’s scientific presence.

About you

In order to set you up for success as a Research Scientist at Wayve, we’re looking for the following skills and experience.

Essential

• Experience in machine learning, with focus in multimodal foundation models and data for foundation models.

• 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 engineering 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

Desirable

• 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.

This role is a full-time role based in Sunnyvale, CA (hybrid) and the reasonably estimated salary for this role ranges from $370,000 to $419,000, plus a competitive equity package. Actual compensation is based on the candidate's skills, qualifications, and experience. At Wayve we want the best of all worlds so we operate 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.