Staff Machine Learning Engineer (Gaia)

Responsibilities

• 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
• 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)

Benefits

• Private healthcare: Choose our optional health insurance for comprehensive coverage for you and your family.
• Paid time off: Paid vacation plus public holidays and additional leave programs, ensuring you have time to unwind.
• Mental health resources: Through Spill, you can access therapy and mental health support.
• Community and socials: Join clubs or attend team socials to connect over hobbies, sports, or just for fun.
• Competitive compensation: Our compensation package includes cash and equity, making you a true partner in our success.
• Learning and development: Budgets for books, courses, and company-wide training to support your continuous growth.

Qualifications

• In-depth experience training large-scale models (language, video, or other foundation models), including ownership of training at scale
• 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
• Strong understanding of model architecture and the ability to contribute meaningfully to architectural/training decisions
• Experience improving data/training pipelines and working across infrastructure constraints (distributed training, efficiency, reliability)
• 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
• Proven technical leadership (tech lead ownership, mentoring, setting direction across an area)
• Direct experience with world models, video generation, or long-horizon prediction

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