Applied Scientist: Generative World Models for Autonomy

Wayve; Greater London; On-site; GBP 90,000 - 130,000; Full time

Job summary

Wayve is seeking a Machine Learning Engineer in Simulation to advance end-to-end autonomous driving research through next-generation world models and efficient generative systems. You will work on GAIA-like models for real-time roll-outs and complex environments, balancing accuracy with compute budgets in a fast-paced team.

You will collaborate across disciplines, publish in top venues, and mentor junior researchers while shaping technical roadmaps and driving innovations in sim-to-real

Qualifications

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

Responsibilities

• Set technical direction and designing key components of the system.
• Deep work / coding: implementing and debugging new modeling capabilities.
• Cross-team collaboration, brainstorming and pairing: you brainstorm ideas with your team mates or pair to solve a difficult problem
• Mentorship / async review: you review a teammate's pull request or experimental design and outcomes and provide feedback
• Sync-ups: share findings with the rest of your team leading to a discussion on designing the next experiments.
• Paper reading/ reading groups: read papers and summarise them for the group.
• Invent next-generation, efficient generative world-models that deliver real-time roll-outs and controllable scene editing.
• Architect interactive world models enabling reinforcement learning, planning and safety evaluation loops.
• Optimise end-to-end performance — reduce inference latency.
• Define robust metrics for long-horizon coherence, physics fidelity and planner integration.
• Ship impact: integrate models into closed-loop training and evaluation; measure sim-to-real gap.
• Mentor and influence: guide junior researchers and shape roadmaps.

Skills

Generative video

Diffusion models

World models

Python

PyTorch

Tools

PyTorch

Job description

Wayve is seeking a Machine Learning Engineer in Simulation to advance end-to-end autonomous driving research through next-generation world models and efficient generative systems. You will work on GAIA-like models for real-time roll-outs and complex environments, balancing accuracy with compute budgets in a fast-paced team.

You will collaborate across disciplines, publish in top venues, and mentor junior researchers while shaping technical roadmaps and driving innovations in sim-to-real