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Principal Machine Learning Engineer GAIA

Wayve · London, United Kingdom · Remote possible

Confidence
High confidence
Posted
6 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.

You'll be joining the Simulation team (Simulation Evaluation & Validation), working on Gaia — Wayve's world model. Gaia is trained on large-scale driving video to predict future frames from past context, functioning as a simulator that generates synthetic scenarios and operates in closed loop with the driving model itself.

The team partners closely with Research, Applications, Core Simulation Engineering, and Cloud/Infrastructure to turn model improvements into measurable downstream impact on the driving stack. Your day-to-day - Own and lead parts of large-scale training for Gaia (language- or video-style training at scale), from experimentation through productionised training runs. - Contribute to and influence model architecture decisions — not just applying existing models. - Partner across sub-teams: Research (model development), Applications (adapting models to use cases), Core Simulation Engineering, and Cloud/Infrastructure. - Translate product and evaluation needs into model improvements (e.g. better long-horizon prediction, scenario generation quality, failure-case coverage).

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