Applied Scientist/Machine Learning Engineer Gaia

Overview

In this role, you will advance Wayve’s end-to-end autonomous driving research by building next‑gen world models and planners for fast, interactive simulation. You’ll enable scalable, real-time rollouts and closed‑loop evaluation to accelerate AV2.0. You’ll operate at the intersection of ML research, multi‑modal modeling, and real-world deployment, shaping how synthetic environments can augment or replace traditional data collection. This position offers impact, collaboration with top researchers, and the chance to influence the future of autonomy.

Pay / Benefits

• hybrid working policy in London
• full-time role based in London
• access to massive driving datasets
• collaboration with world‑class research talent
• high‑trust, high‑autonomy team
• opportunity to publish and shape future of generative AI for autonomy

Responsibilities

• Invent efficient generative world‑models (diffusion, transformer, or hybrid) enabling real‑time rollouts and controllable scene editing
• Architect interactive world models allowing reinforcement learning, planning, and safety evaluation loops
• Optimize end‑to‑end performance from latent compression to context pruning to reduce inference latency
• Define metrics for long‑horizon coherence, physics fidelity, and planner integration; perform ablations and scaling studies
• Ship models into closed‑loop training and evaluation; measure sim‑to‑real gaps against on‑road driving results
• Mentor junior researchers, shape technical roadmaps, publish at top venues, and represent Wayve in the community
• Challenge assumptions and drive innovation through ablations and exploration of new training/evaluation approaches

Key requirements

• 4+ years of ML research/engineering with focus on generative video and world models
• Deep knowledge of diffusion and latent‑video models; track record of improving sampling efficiency or throughput
• Experience with high‑dimensional temporal/spatial data (e.g., video, multi‑sensor fusion)
• Strong Python and PyTorch engineering fundamentals; experience building research‑grade production tools
• Strong publication record or contributions to open‑source ML tooling
• Ability to work collaboratively in a fast, interdisciplinary team environment

• collaborative
• adaptable/in fast-paced environments
• strong communication
• diffusion models
• latent-video models
• generative video

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