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Applied Scientist/Machine Learning Engineer, Gaia

Wayve · London, United Kingdom

Confidence
High confidence
Posted
2 Oct 2026
First seen
24 Oct 2026
Last checked

Evidence

High confidence

The posting says Wayve sponsors visas, and Wayve is on the UK Home Office register.

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UK Home Office Register of Licensed Sponsors

Listed as Wayve Technologies Ltd. London · Skilled Worker route · A rating. Snapshot 26 Oct 2026.

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Job posting

Relocation support and visa sponsorship where applicable

On jobs.ashbyhq.com/wayve, read 26 Oct 2026.

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Seen on the job board

First seen 24 Oct 2026. We check every day and remove closed roles.

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jobs.ashbyhq.com/wayve

Wayve’s job board, hosted by Ashby.

Being on the register means Wayve can sponsor visas. It doesn’t confirm this role qualifies, so check with the recruiter.

About the role

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.

Generative Simulation is the team advancing our end-to-end autonomous driving research, incubating and investing in new ideas that can become game-changing technological advances for Wayve. As a Machine Learning Engineer in the team, you will play a key role in developing next-generation world models and planners, such as GAIA, that can simulate complex, diverse, and temporally consistent driving environments to power faster training, broader testing, and scalable deployment, even in areas and scenarios we have never driven in before.

As we push toward the next generation of GAIA, efficiency and interactivity are a major focus: models must run thousands of roll-outs per second, support closed-loop agent interaction, and fit within practical compute budgets. You will work at the intersection of machine learning research, multi-modal modelling, and real-world deployment, tackling questions such as how we can deploy autonomous vehicles in a new geography without collecting any real-world data, and whether synthetically generated environments can fully replace physical testing and data collection.

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