Staff ML Engineer Gaia

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.

The role

Gaia is Wayve’s video world model: trained on large-scale driving video, it predicts future frames from past context—functioning as a simulator that helps generate synthetic scenarios, including rare or safety-critical events. As a Staff ML Engineer on Gaia, you’ll own and drive work on training and improving frontier-scale models trained in-house. This is a high-impact role with the opportunity to tech-lead a key area and help shape the next version of Gaia in a fast-paced, results-focused environment.

Key responsibilities:

Lead and execute large-scale training runs for video (or adjacent) foundation models, from experimental design through production-grade execution

Contribute to model architecture and training strategy, using first-principles understanding rather than “off-the-shelf” application

Improve world-model capabilities that enable synthetic scenario generation and downstream evaluation/training of the driving model

Partner closely with research, applications, simulation engineering, and cloud/infrastructure teams to deliver end-to-end impact

Provide technical leadership through mentorship, review, and setting high engineering/research standards (Senior/Staff scope)

About you

In order to set you up for success as a Staff ML Engineer (Gaia) at Wayve, we’re looking for the following skills and experience.

Essential

In-depth experience training large-scale models (language, video, or other foundation models), including ownership of training at scale

Strong understanding of model architecture and the ability to contribute meaningfully to architectural/training decisions

Strong hands-on engineering skills with modern ML stacks (e.g., PyTorch), including debugging and performance/reliability-minded development

Relevant industry experience (typically 4–5+ years); advanced degrees are valued, but depth of applied experience is important

Desirable

Direct experience with world models, video generation, or long-horizon prediction

Experience improving data/training pipelines and working across infrastructure constraints (distributed training, efficiency, reliability)

Proven technical leadership (tech lead ownership, mentoring, setting direction across an area)

This is a full-time role based in our office in London. At Wayve we want the best of all worlds so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, and time spent working from home.

A quick, honest note before you apply.

Wayve is not a mature, fully-structured place with the playbook already written. Much of how we work is still being written, and if you join, you’ll help write it. That suits people who want real ownership more than people who need a settled structure from day one.

If that sounds like the kind of problem you want to spend your time on, we’d really like to hear from you.