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About the Role
Gaia is Wayve’s video world model, functioning as a simulator that predicts future frames from past context. As a Staff ML Engineer on Gaia, you will 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.
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).
Requirements
- 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.
- 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).
Skills
- PyTorch
Location
- London
Work Type
- Full-time
- Hybrid
Experience Level
- Staff
- Senior
- 4-5+ years
Education Level
- Advanced degrees valued