Principal Machine Learning Engineer GAIA
Salary not listed • London, United Kingdom • Full-time
Posted 24d ago
Remote Work Policy
On-site
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
Wayve is building the leading AI platform for autonomous driving, pioneering an end-to-end AI approach that enables vehicles to learn directly from real-world experience. This role is within the Simulation team, focusing on Gaia, Wayve's world model. Gaia is trained on large-scale driving video to predict future frames, functioning as a simulator that generates synthetic scenarios and operates in closed loop with the driving model. The team collaborates closely with Research, Applications, Core Simulation Engineering, and Cloud/Infrastructure to translate model improvements into tangible impacts on the driving stack. You will own and lead parts of large-scale training for Gaia, contribute to model architecture decisions, and mentor others to raise the engineering and research quality bar.
Responsibilities
- Lead and execute Gaia's post-training and closed-loop pipeline, including fine-tuning and aligning the world model.
- Push Gaia's autoregressive generation towards longer, more stable rollouts and ensure the model is deployment-ready.
- Contribute to broader model architecture and training-strategy decisions.
- Partner with research, applications, simulation engineering, and cloud/infrastructure teams.
- Provide technical leadership through mentorship, review, and setting high engineering/research standards.
- Own and lead parts of large-scale training for Gaia, from experimentation through productionised runs.
- Contribute to and influence model architecture decisions, not just applying existing models.
- Translate product and evaluation needs into model improvements.
Requirements
- Hands-on experience post-training/fine-tuning large-scale models (language, video, or other foundation models).
- Experience with world models, autoregressive generation, and long-horizon generation.
- Experience with diffusion/flow models and a solid understanding of 3D vision.
- Strong understanding of model architecture and ability to contribute to architectural/training decisions.
- Strong hands-on engineering skills with modern ML stacks (e.g., PyTorch), including debugging and performance/reliability-minded development.
- 5+ years of relevant industry experience.
- Experience with inference optimisation or deploying large models under latency/compute constraints (desirable).
- Experience improving data/training pipelines and working across infrastructure constraints (desirable).
- Proven technical leadership, including tech lead ownership, mentoring, or setting direction (desirable).