About the job
Gaia is Wayve’s video world model: trained on large-scale driving video, it predicts future frames from past context—functioning as a simulator that helps generate synthetic scenarios, including rare or safety-critical events. As a Staff ML Engineer on Gaia, you’ll 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)
Qualifications
Minimum
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
Preferred
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)