Staff Machine Learning Engineer (Gaia)

Join Wayve, a pioneering company in the field of self-driving cars. As a Staff Machine Learning Engineer, you will lead and execute large-scale training runs for video foundation models, contribute to model architecture and training strategy, and improve world-model capabilities. You will work closely with various teams to deliver end-to-end impact and provide technical leadership through mentorship and setting high engineering/research standards. Enjoy a competitive compensation package, private healthcare, paid time off, mental health resources, and opportunities for learning and development.

• Lead and execute large-scale training runs for video 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.

• We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. If you’re passionate about self-driving cars and think you have what it takes to make a positive impact on the world, we encourage you to apply
• Strong understanding of model architecture and the ability to contribute meaningfully to architectural/training decisions
• In order to set you up for success as a Staff ML Engineer (Gaia) at Wayve, we’re looking for the following skills and experience
• Relevant industry experience (typically 4–5+ years); advanced degrees are valued, but depth of applied experience is important
• Strong hands-on engineering skills with modern ML stacks (e.g., PyTorch), including debugging and performance/reliability-minded development
• In-depth experience training large-scale models (language, video, or other foundation models), including ownership of training at scale
• 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)