Machine Learning Scientist/Engineer

Engg; Sunnyvale (CA); Hybrid; USD 180,000 - 230,000; Full time

Job summary

Wayve is building the embodied intelligence platform for autonomous robots, blending vision, language and robot interaction. We seek a Research Scientist to join the MEGA team as a founding member, shaping foundation-model learning for robots and evaluating real-world performance with a growing fleet of demonstrations.

You will collaborate with research scientists, ML engineers, roboticists and hardware teams to design architectures, objectives and data strategies, while pushing scalable

Qualifications

• Experience in machine learning with multimodal foundation models.
• Experience with scalable multi-node training and large datasets.
• Strong publication record in top venues (ICLR, NeurIPS, CVPR, etc.).
• Hands-on with modern ML frameworks.

Responsibilities

• Design and run experiments on model architectures, learning objectives, and data strategies for robot foundation models.
• Develop scalable pre-training and post-training methods using web video, egocentric video, and robot-interaction data.
• Curate, filter, and evaluate large-scale robotics datasets.
• Build and use distributed training pipelines for large models and multimodal data.
• Collaborate with robotics and hardware teams to connect progress to real-world performance.
• Communicate findings through internal reviews and publications.

Skills

Multimodal models

Scalable training

Research track record

Engineering skills

Experiment design

Research-to-prod

Communication

PyTorch

TensorFlow

Tools

PyTorch

TensorFlow

Job description

Before the detail, here's the challenge you'd help us solve. We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that. Here’s what this particular role covers.

About our Science Teams

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: models that learn from large-scale video, language, and robot-interaction data, then generalize across tasks and embodiments, including mobile manipulators, dual-arm platforms, and humanoids. Our aim is to build agents that can perceive, reason about, and act reliably in the physical world. You will help define and build the foundation-model learning stack for robotics: novel model architectures, pre-training objectives, post-training methods, and scalable data and training systems. The work combines frontier ML research with a direct route to real-world evaluation on a growing fleet of robots. Your work may span 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 increasingly capable, robust, and general robot policies. You will collaborate with research scientists, ML engineers, roboticists, and hardware teams to turn promising ideas into large-scale experiments, strong research contributions, and compelling robot demonstrations.

Your day-to-day

• 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.

What you’ll be working on

• 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.

You should apply if

• 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, 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.

Not ticking every box? That’s totally okay! If you’re passionate about autonomy and keen to learn, we encourage you to apply even if you don’t meet every requirement.

More about Wayve

Wayve is building the leading AI platform for autonomous driving. We are pioneering an end to end AI approach that enables vehicles to learn directly from real world experience, developing the ability to adapt, generalise and improve at scale. Instead of relying on hand coded rules or pre mapped environments, our AI Driver learns to drive by understanding the world around it. The result is technology that navigates complex urban environments with intelligence, precision and natural flow, unlocking meaningful advances in both safety and efficiency. We believe autonomy represents a once in a generation transformation in how people and goods move, comparable to the shift from horses to cars, and from human driven vehicles to intelligent machines. Our ambition is to make autonomy universal.

Wayve’s mapless and hardware agnostic AI platform integrates with global OEM partners, enabling continuous software evolution and unlocking advanced levels of automation from L2 plus through to L4 as our core AI model scales. In a race increasingly defined by intelligence and real world learning, Wayve is taking a distinct approach, building a generalisable driving intelligence that can power any vehicle, anywhere. By combining embodied AI with scalable deployment, we are creating technology that can be shaped to each OEM brand and driver experience, accelerating the transition to a safer, more intelligent future of mobility.

How we work

Locations & Flexible Working: Our main hubs are in London, Sunnyvale, Yokohama, Herzliya, Vancouver and Leonberg. We operate a hybrid working model that combines in-person collaboration in our dedicated office spaces with focused time working remotely. This gives our teams the connection and energy of