wayve - Principal Machine Learning Engineer GAIA

• 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 • 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). • 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.