Staff Machine Learning Scientist/Engineer

Wayve•Sunnyvale, CA

5d•Hybrid

About The Position

Wayve is seeking a founding member for the Multi-Embodiment Generalist Agent (MEGA) team within Wayve Science. This team is focused on building foundation models for general-purpose robots. These models will learn from diverse data sources including video, language, and robot interactions, and will generalize across different robotic embodiments such as mobile manipulators, dual-arm platforms, and humanoids. The goal is to create agents capable of perceiving, reasoning about, and acting reliably in the physical world. The role involves defining and building the learning stack for these foundation models, including novel architectures, pre-training objectives, post-training methods, and scalable data and training systems. This position offers a blend of cutting-edge ML research with direct real-world evaluation on a fleet of robots. Work may encompass vision-language-action models, world and action models, video and multimodal models, imitation learning, reinforcement learning, and self-supervised learning. The scientist will utilize large-scale video and robotics datasets and distributed training infrastructure to develop advanced robot policies. Collaboration with research scientists, ML engineers, roboticists, and hardware teams is essential to translate research into experiments and demonstrations. This is a unique opportunity to take significant ownership of a new ML-first research program at the forefront of foundation models and embodied intelligence.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.
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.