Staff ML Engineer, Gaia

Wayve Wayve · Robotics · London, United Kingdom · Simulation, Evaluation, Validation

Staff ML Engineer on Gaia, Wayve's video world model, responsible for leading and executing large-scale training runs for frontier foundation models, contributing to model architecture and training strategy, and improving world-model capabilities for synthetic scenario generation and downstream training of the driving model. This role involves technical leadership and collaboration with research, applications, simulation engineering, and cloud/infrastructure teams.

What you'd actually do

  1. Lead and execute large-scale training runs for video (or adjacent) foundation models, from experimental design through production-grade execution
  2. Contribute to model architecture and training strategy, using first-principles understanding rather than “off-the-shelf” application
  3. Improve world-model capabilities that enable synthetic scenario generation and downstream evaluation/training of the driving model
  4. Partner closely with research, applications, simulation engineering, and cloud/infrastructure teams to deliver end-to-end impact
  5. Provide technical leadership through mentorship, review, and setting high engineering/research standards (Senior/Staff scope)

Skills

Required

  • In-depth experience training large-scale models (language, video, or other foundation models)
  • ownership of training at scale
  • Strong understanding of model architecture
  • ability to contribute meaningfully to architectural/training decisions
  • Strong hands-on engineering skills with modern ML stacks (e.g., PyTorch)
  • debugging and performance/reliability-minded development
  • Relevant industry experience (typically 4–5+ years)

Nice to have

  • Direct experience with world models, video generation, or long-horizon prediction
  • Experience improving data/training pipelines
  • working across infrastructure constraints (distributed training, efficiency, reliability)
  • Proven technical leadership (tech lead ownership, mentoring, setting direction across an area)

What the JD emphasized

  • ownership of training at scale
  • first-principles understanding
  • technical leadership

Other signals

  • video world model
  • foundation models
  • large-scale training runs
  • synthetic scenario generation