Principal Machine Learning Engineer GAIA

Role details

Before the detail, here's the challenge you'd help us solve. We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that. Here’s what this particular role covers. The role Gaia is Wayve's world model: trained on large-scale driving video, it predicts future frames from past context functioning as a simulator that generates synthetic scenarios, and operating in closed loop with the driving model itself. As a Principal ML Engineer/ Applied Scientist, 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 focus areas are getting Gaia stable and coherent over long autoregressive rollouts, and making it reliably steerable towards the behaviours we need using signal from evaluation, and driving-model training. Key responsibilities - Lead and execute Gaia's post-training and closed-loop pipeline, fine-tuning and aligning the world model through post-training experimentation and targeted data curation. - Push Gaia's autoregressive generation towards longer, more stable rollouts, and make the model deployment-ready, inference time and reliability included. - Contribute to broader model architecture and training-strategy decisions where they intersect with pre- and post-training and the application layer. - Partner closely with research, applications, simulation engineering, and cloud/infrastructure teams to translate post-training improvements into measurable downstream impact. - Provide technical leadership through mentorship, review, and setting high engineering/research standards. About you — Essential - Hands-on experience post-training/fine-tuning large-scale models (language, video, or other foundation models) - Experience with world models, autoregressive generation, and long-horizon generation. - Experience with diffusion/flow models and understanding of 3D vision. - 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 5+ years); advanced degrees are valued, but depth of applied experience is important Desirable - Experience with inference optimisation or deploying large models under latency/compute constraints - 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 role is a full-time role based in London, UK (hybrid). 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. We operate core working hours so you can determine the schedule that works best for you and your team. A quick, honest note before you apply. Wayve is not a mature, fully-structured place with the playbook already written. Much of how we work is still being written, and if you join, you’ll help write it. That suits people who want real ownership more than people who need a settled structure from day one. If that sounds like the kind of problem you want to spend your time on, we’d really like to hear from you.