Senior Generative ML Engineer - Simulation for AVs

EngineersOfAI

Greater London

Hybrid

GBP 90,000 - 150,000

Full time

14 days+

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Job summary

Wayve is seeking a Machine Learning Engineer in the Simulation team to advance world models and planners for end‑to‑end autonomous driving research. You will drive generative simulation that enables faster training, broader testing, and scalable deployment across diverse environments.

As GAIA evolution continues, you will focus on efficient, interactive models with large‑scale rollouts and closed‑loop agent interactions, addressing new geography generalization and synthetic data challenges.

Qualifications

  • Experience building generative world models for simulation tasks.
  • Strong understanding of planning and RL for control in simulated environments.
  • Ability to measure and improve model fidelity and stability.

Responsibilities

  • Invent next‑generation, efficient generative world models for real‑time rollouts.
  • Architect interactive world models enabling RL, planning and safety loops.
  • Optimise end‑to‑end performance to reduce inference latency.
  • Define metrics for coherence, physics fidelity and integration with planners.
  • Ship models into closed‑loop training and evaluation pipelines.
  • Mentor junior researchers and help shape technical roadmaps.
  • Challenge assumptions and pursue bold ideas for training and evaluation.

Skills

Generative models
World models
Reinforcement learning
Planning
Safety evaluation

Tools

Diffusion models
Transformers

Job description

Wayve is seeking a Machine Learning Engineer in the Simulation team to advance world models and planners for end‑to‑end autonomous driving research. You will drive generative simulation that enables faster training, broader testing, and scalable deployment across diverse environments.

As GAIA evolution continues, you will focus on efficient, interactive models with large‑scale rollouts and closed‑loop agent interactions, addressing new geography generalization and synthetic data challenges.

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