Autonomous Driving World-Model ML Engineer

Wayve; London (KY); Hybrid; USD 119,000 - 172,000; Full time

Job summary

Wayve in London is hiring a Machine Learning Engineer specializing in world models and synthetic data generation to accelerate autonomous driving research. You will build GAIA-class models, scale data pipelines, and bridge them into high-throughput training infrastructure.

You'll collaborate with researchers and platform engineers to drive end-to-end workflows from config to training artefacts, and help deploy models across new vehicle embodiments while improving throughput and reliability.

Qualifications

• 4+ years in applied ML / research engineering with neural nets training and shipping.
• Strong Python and PyTorch; GPU training, debugging, and reading model code.
• Hands-on with video, generative, or world models.
• Working knowledge of cameras and 3D geometry and why they break generation.
• Evidence of taking generated or simulated data into a trained downstream model and measuring impact.
• Ability to operate multi-GPU training and scale workflows.

Responsibilities

• Post-train and iterate GAIA-class world models for synthetic-data capabilities and conditioning.
• Own the generation loop: config → large-scale GPU inference → training-ready artefacts.
• Land synthetic data in driving-model training with quality filters and experiments.
• Diagnose and fix geometry, calibration, and controllability failures affecting training data.
• Improve throughput and yield: inference optimisations and self-serve workflows.
• Expand coverage to new vehicle platforms and safety-critical scenarios.
• Partner with world-model researchers, infra, and driving-model owners for unified workflows.

Skills

Python

PyTorch

ML research

Video / world models

Multi-GPU training

Tools

Flyte

Ray

Spark

Job description

Wayve in London is hiring a Machine Learning Engineer specializing in world models and synthetic data generation to accelerate autonomous driving research. You will build GAIA-class models, scale data pipelines, and bridge them into high-throughput training infrastructure.

You'll collaborate with researchers and platform engineers to drive end-to-end workflows from config to training artefacts, and help deploy models across new vehicle embodiments while improving throughput and reliability.