Principal Machine Learning Engineer GAIA

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.   🛠️ About our Simulation Teams You'll be joining the Simulation team (Simulation Evaluation & Validation), working on Gaia — Wayve's world model. Gaia is trained on large-scale driving video to predict future frames from past context, functioning as a simulator that generates synthetic scenarios and operates in closed loop with the driving model itself. The team partners closely with Research, Applications, Core Simulation Engineering, and Cloud/Infrastructure to turn model improvements into measurable downstream impact on the driving stack.   🧠 Your day-to-day Own and lead parts of large-scale training for Gaia (language- or video-style training at scale), from experimentation through productionised training runs. Contribute to and influence model architecture decisions — not just applying existing models. Partner across sub-teams: Research (model development), Applications (adapting models to use cases), Core Simulation Engineering, and Cloud/Infrastructure. Translate product and evaluation needs into model improvements (e.g. better long-horizon prediction, scenario generation quality, failure-case coverage). Mentor and set technical direction, raising the engineering and research quality bar across the team.   🧩 What you’ll be working on Leading and executing Gaia's post-training and closed-loop pipeline — fine-tuning and aligning the world model through post-training experimentation and targeted data curation. Pushing Gaia's autoregressive generation towards longer, more stable rollouts, and making the model deployment-ready (inference time and reliability included). Contributing to broader model architecture and training-strategy decisions where they intersect with pre- and post-training and the application layer. Partnering closely with research, applications, simulation engineering, and cloud/infrastructure teams to translate post-training improvements into measurable downstream impact. Providing technical leadership through mentorship, review, and setting high engineering/research standards.   🙌 You should apply if Essential: You have hands-on experience post-training/fine-tuning large-scale models (language, video, or other foundation models). You have experience with world models, autoregressive generation, and long-horizon generation. You have experience with diffusion/flow models and a solid understanding of 3D vision. You have a strong understanding of model architecture and can contribute meaningfully to architectural/training decisions. You have strong hands-on engineering skills with modern ML stacks (e.g. PyTorch), including debugging and performance/reliability-minded development. You have 5+ years of relevant industry experience (advanced degrees are valued, but depth of applied experience matters more). 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, or setting direction across an area.   🌱 Not ticking every box? That’s totally okay! If you’re passionate about autonomy and keen to learn, we encourage you to apply even if you don’t meet every requirement.   More about Wayve: 🚀 Wayve is building the leading AI platform for autonomous driving. We are pioneering an end to end AI approach that enables vehicles to learn directly from real world experience, developing the ability to adapt, generalise and improve at scale. Instead of relying on hand coded rules or pre