Principal Machine Learning Engineer GAIA

Wayve is building an AI platform for autonomous driving that enables vehicles to learn from real-world experience and adapt at scale. The Principal Machine Learning Engineer will lead large-scale training, post-training, and closed-loop development for Gaia, Wayve's world model, while contributing to model architecture, deployment readiness, cross-functional collaboration, and technical mentorship.

Responsibilities:

• 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)
• Mentor and set technical direction, raising the engineering and research quality bar across the team
• Leading and executing Gaia's post-training and closed-loop pipeline — fine-tuning and aligning the world model through post-training experimentation and targeted data curation
• Pushing Gaia's autoregressive generation towards longer, more stable rollouts, and making the model deployment-ready (inference time and reliability included)
• Contributing to broader model architecture and training-strategy decisions where they intersect with pre- and post-training and the application layer
• Partnering closely with research, applications, simulation engineering, and cloud/infrastructure teams to translate post-training improvements into measurable downstream impact
• Providing technical leadership through mentorship, review, and setting high engineering/research standards