Research Scientist, Robot Foundation Model

Wayve•Sunnyvale, CA

2d•$370,000 - $419,000•Hybrid

About The Position

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 beyond not self-driving vehicles. Our goal is to create intelligent agents that can perceive, reason, move and manipulate the physical world across diverse embodiments, including mobile manipulators, dual-arm platforms and humanoids. This is a rare opportunity to help build a new robotics program from the ground up while working closely with experienced researchers in foundation models, embodied intelligence and large-scale machine learning. You will have meaningful ownership of research projects, contribute to the team’s technical direction and see your ideas tested on real robotic systems. You will work across the robot-learning stack: developing model architectures and learning algorithms, building scalable data and training pipelines, designing evaluations and deploying policies on physical robots. The goal is to produce both strong research and increasingly general, robust and useful real-world capabilities. Your day-to-day work may span vision-language-action models, world and action models, multimodal and omni models, video models, reinforcement learning, imitation learning and behavioral cloning. You will work with large-scale video and robotics datasets, distributed training infrastructure and a growing fleet of robotic platforms. You will collaborate with research scientists, ML engineers, roboticists and hardware teams to turn promising ideas into large-scale experiments and compelling robot demonstrations. The role offers substantial room to learn, grow and develop expertise across both frontier machine learning and real-world robotics. This is a rare opportunity to work on general robotics at the foundation-model frontier and help shape intelligent systems that act in the physical world.

Requirements

Experience in machine learning, with focus in one or more of: vision-language models, video models, robot policies, foundation models for robotics or embodied AI.
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 coding 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 model architectures, data and learning approaches for robot foundation models.
Design, implement and evaluate models such as VLAs, WAMs, omni-modal models, video models and related foundation-model architectures for robotics.
Explore and develop learning approaches including reinforcement learning, behavioural cloning and other methods relevant to robot policy development.
Synthesize, curate and filter large-scale video datasets for model training and evaluation.
Build and use scalable distributed training pipelines and infrastructure for large models and large datasets.
Collaborate closely with scientists, engineers and robotics teams to connect research progress to real-world robot performance.
Communicate research clearly internally and, where appropriate, contribute to external publications and Wayve’s scientific presence.