Staff Machine Learning Scientist/Engineer

Wayve•Sunnyvale, CA

8d•$370,000 - $419,000•Hybrid

About The Position

The role involves joining the Multi-Embodiment Generalist Agent (MEGA) team as a founding member. MEGA is focused on building foundation models for general-purpose robots, utilizing large-scale video, language, and robot-interaction data to create models that can generalize across different tasks and embodiments. The goal is to develop agents capable of perceiving, reasoning about, and acting reliably in the physical world. The position will contribute to defining and building the foundation-model learning stack for robotics, encompassing novel model architectures, pre-training objectives, post-training methods, and scalable data and training systems. This role combines frontier ML research with direct real-world evaluation on a fleet of robots. Work may involve vision-language-action models, world and action models, video and multimodal models, imitation learning, reinforcement learning, and self-supervised learning, using large-scale datasets and distributed training infrastructure to develop robust robot policies. Collaboration with research scientists, ML engineers, roboticists, and hardware teams is expected to translate ideas into experiments and demonstrations. This is an opportunity for significant ownership in an 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.
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.