Machine Learning Engineer Synthetic Data
Wayve is a London-headquartered embodied-AI company developing and licensing mapless, vehicle-agnostic driving software for assisted, automated, and robotaxi applications.
About Wayve
Wayve Technologies Ltd. develops the Wayve AI Driver, an end-to-end, data-trained software platform that runs on onboard vehicle compute and native sensors. It is designed for OEM integration across L1 driver assistance through L4 automated driving, without HD maps.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will post-train and optimize world models, generate multimodal synthetic driving data, and integrate it into model-training workflows. You will diagnose geometry and controllability failures, improve GPU inference throughput, expand vehicle and safety-scenario coverage, and partner on generation, evaluation, and training systems.
Requirements
- 4+ years in applied machine learning or research engineering
- Python
- PyTorch
- GPU training
- Neural network training
- Video model experience
- Generative model experience
- World model experience
- 3D geometry
- Multi-camera rig knowledge
- Camera intrinsics
- Camera extrinsics
- Novel-view synthesis
- Neural rendering
- Synthetic-data evaluation
- Multi-GPU job experience
- Workflow orchestration
- Large video artefact management
- Diffusion
- Flow matching
- Autoregressive video
- Distillation
- KV caching
- Autonomous vehicle systems
- Robotics
- Simulation
- Flyte
- Ray
- Spark
- Reward model
- Offline reinforcement learning
- Cloud GPU infrastructure
- Distributed training
Responsibilities
- Post-train and iterate world models for synthetic-data capabilities
- Own large-scale GPU generation workflows and training-ready artefacts
- Integrate synthetic data into driving-model training and measure its impact
- Diagnose geometry, calibration, and controllability failures
- Improve inference throughput, generation yield, and self-service workflows
- Expand coverage to vehicle platforms and safety-critical scenarios
- Partner on generation, evaluation, and training workflows
Benefits
- Hybrid working policy
- Core working hours with schedule flexibility