Machine Learning Scientist/Engineer

Wayve • Sunnyvale, CA

1d • Hybrid

About The Position

Wayve is seeking a Research Scientist to join the Multi-Embodiment Generalist Agent (MEGA) team within Wayve Science as a founding member. MEGA is developing foundation models for general-purpose robots that learn from large-scale video, language, and robot-interaction data and generalize across tasks and embodiments, including mobile manipulators, dual-arm platforms, and humanoids. The team aims to build agents that can perceive, reason about, and act reliably in the physical world.

The role will help define and build the foundation-model learning stack for robotics, including model architectures, pre-training objectives, post-training methods, and scalable data and training systems. The work combines machine-learning research with real-world evaluation on Wayve’s growing robot fleet, and may cover 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 robust, increasingly capable robot policies.

You will collaborate with research scientists, ML engineers, roboticists, and hardware teams to translate ideas into large-scale experiments, research contributions, and robot demonstrations. Wayve operates a hybrid working model, combining collaboration in office spaces with remote work. The role may include hands-on work in vehicle workshops and labs. Relocation support and visa sponsorship may be available where applicable.

Requirements

• Experience in machine learning, focused on multimodal foundation models and data for foundation models.
• Experience with scalable training, such as multi-node training, large datasets, 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 ability to share research clearly across teams.

Responsibilities

• 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.
• Develop self-supervised and generative pre-training methods for robot foundation models.
• Develop post-training approaches, including supervised fine-tuning, imitation learning, and reinforcement learning, to improve real-world robot capabilities.
• Curate, filter, mix, and evaluate large-scale robotics datasets, including egocentric, UMI, and teleoperated data.
• Develop and use distributed training pipelines for large models and multimodal datasets.
• Work with robotics and hardware teams to connect model progress to measurable real-world performance.
• Communicate research findings through internal reviews, publications where appropriate, and robot demonstrations.

Benefits

• Meaningful equity.
• Relocation support and visa sponsorship where applicable.
• Hybrid working and core hours.
• Learning and development budgets, with support for training, conferences, and growth.
• Comprehensive benefits, including health insurance, dental coverage, enhanced maternity and paternity leave, retirement or pension where applicable, access to therapists, wellbeing partnerships, and team socials.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed