Staff ML Engineer, Gaia in London

Founded in 2017, Wayve is the leading developer of Embodied AI technology. Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems. Our vision is to create autonomy that propels the world forward. Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving. In our fast‑paced environment, big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future. At Wayve, your contributions matter. We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact.

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.

Key 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).

About You

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.

Essential

• 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.

Desirable

• 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).

This is a full‑time role based in our office in London. At Wayve we want the best of all worlds so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, and time spent working from home.

Wayve is committed to creating an inclusive interview experience. If you require any accommodations or adjustments to participate fully in our interview process, please let us know. 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.

At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law.