Machine Learning Scientist/Engineer

Join Wayve as a founding Research Scientist on the MEGA team to build foundation models for general-purpose robots. You will shape architectures, objectives, and data strategies that scale across embodiments and real-world evaluation. Collaborate with ML engineers, roboticists, and hardware teams to translate research into measurable robot performance. This role blends frontier ML with hands-on robotics, offering impactful work from model design to demonstrations in a growing fleet of robots. You will contribute to a mission of universal autonomous robotics through scalable, multimodal learning pipelines.

• hybrid working
• learning and development budgets
• relocation support and visa sponsorship
• comprehensive health benefits (health, dental)
• enhanced maternity/paternity leave
• retirement/pension where applicable

• 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

• Experience in machine learning with a focus on 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 with publications in top-tier venues (ICRA, CoRL, CVPR, NeurIPS, ICML, ICLR)
• Strong engineering skills with modern ML frameworks
• Ability to design and run rigorous experiments while collaborating with engineering and robotics teams
• Experience translating research ideas into working systems, experiments or deployed capabilities
• Strong communication skills to share research across teams

• collaboration across cross-functional teams
• clear technical communication
• proactive learning attitude
• multimodal foundation models
• self-supervised learning
• generative pre-training methods