ML Engineer, Synthetic Data for Autonomous Driving

Wayve; Greater London; Hybrid; GBP 110,000 - 150,000; Full time

Benefits offered by this job

Hybrid working policy

London office

Job summary

Wayve in London is seeking a senior ML/Research Engineer to build, scale, and optimise GAIA-class world models and bridge them into high-throughput generation and training pipelines. You will post-train world models for new embodiments and behaviours, generate multimodal synthetic data, and land that data in our driving-model training stack.

You will sit between ML research and engineering, collaborating with scientists on architecture and conditioning, and with platform engineers on generation

Qualifications

• 4+ years in applied ML / research engineering.
• Strong Python and PyTorch; comfortable with GPU training and reading model code.
• Hands-on experience with video, generative, or world models.

Responsibilities

• Post-train and iterate GAIA-class world models for synthetic-data capabilities including rig transfer and pose transfer.
• Own the generation loop from config to training-ready artefacts with clear lineage.
• Land synthetic data in driving-model training with quality filters and experiments.
• Diagnose and fix geometry/calibration failures that affect training-grade video.
• Improve throughput and yield via 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 to keep generation, evaluation, and training aligned.

Skills

Python

PyTorch

GPU training

Video models

3D geometry

Collaboration

Tools

Flyte

Ray

Spark

Job description

Wayve in London is seeking a senior ML/Research Engineer to build, scale, and optimise GAIA-class world models and bridge them into high-throughput generation and training pipelines. You will post-train world models for new embodiments and behaviours, generate multimodal synthetic data, and land that data in our driving-model training stack.

You will sit between ML research and engineering, collaborating with scientists on architecture and conditioning, and with platform engineers on generation