Senior ML Engineer: Generative World Models for Autonomy

AI Startups UK; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Benefits offered by this job

Hybrid work policy

Impactful mobility tech

World-class research talent

Opportunities to publish

Job summary

Wayve in London is seeking a senior ML Engineer for the Simulation team to develop next-generation world models and planners that enable fast, scalable autonomous driving simulations. You will push for efficient, closed-loop training and evaluation across diverse driving environments.

The role emphasizes a strong publication record, collaboration in a fast-paced research setting, and contributions to production-grade tooling.

Qualifications

• 4+ years in ML research/engineering, focused on generative video or world models.
• Deep knowledge of diffusion and latent-video models with efficiency improvements.
• Strong Python and PyTorch fundamentals; experience with research-grade tooling.
• Strong publication record or contributions to open-source ML tooling.

Responsibilities

• Invent next-generation, efficient world-models (diffusion, transformer or hybrid) for real-time roll-outs.
• Architect interactive world models enabling reinforcement learning, planning and safety evaluation loops.
• Optimize end-to-end performance to reduce inference latency.
• Define robust metrics for long-horizon coherence and planner integration; run ablations and scaling studies.
• Ship impact: integrate models into closed-loop training and evaluation; measure sim-to-real gap.
• Mentor and influence: guide junior researchers, shape road-maps, publish at top venues.
• Challenge assumptions and drive innovation; propose bold ideas and conduct ablations.

Skills

Generative video

World models

Diffusion models

PyTorch

Python

Research tooling

Collaboration

Job description

Wayve in London is seeking a senior ML Engineer for the Simulation team to develop next-generation world models and planners that enable fast, scalable autonomous driving simulations. You will push for efficient, closed-loop training and evaluation across diverse driving environments.

The role emphasizes a strong publication record, collaboration in a fast-paced research setting, and contributions to production-grade tooling.