SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Wayve is building an AI platform for autonomous driving using embodied intelligence and end-to-end learning. You'll join the Simulation team (Simulation Evaluation & Validation) working on Gaia, Wayve's world model—a large-scale video-trained foundation model that predicts future frames and generates synthetic driving scenarios in closed loop with the driving model.
In this role, you'll own and lead critical parts of Gaia's large-scale training pipeline, from experimentation through production. You'll contribute to and influence model architecture decisions, not just apply existing frameworks. Your day-to-day involves:
- Leading and executing Gaia's post-training and closed-loop pipeline, including fine-tuning, alignment through post-training experimentation, and targeted data curation.
- Pushing autoregressive generation toward longer, more stable rollouts and making the model deployment-ready (inference time and reliability).
- Contributing to model architecture and training-strategy decisions at the intersection of pre- and post-training and the application layer.
- Partnering closely with Research (model development), Applications (use-case adaptation), Core Simulation Engineering, and Cloud/Infrastructure teams to translate improvements into measurable downstream impact on the driving stack.
- Providing technical leadership through mentorship, code review, and raising engineering and research standards across the team.
You'll work in a hybrid model across Wayve's main hubs (London, Sunnyvale, Yokohama, Herzliya, Vancouver, Leonberg), with core hours in dedicated office spaces and flexibility for remote focus work.
Requirements:
Essential:
- 5+ years of relevant industry experience in machine learning (advanced degrees valued, but applied depth prioritized).
- Hands-on experience with post-training/fine-tuning of 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 solid understanding of 3D vision.
- Strong understanding of model architecture with ability to contribute meaningfully to architectural and training decisions.
- Strong hands-on engineering skills with modern ML stacks (e.g., PyTorch), including debugging and performance/reliability-minded development.
Desirable:
- Experience with inference optimization 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).
