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Wayve is building the leading AI platform for autonomous driving, using end-to-end AI that enables vehicles to learn from real-world experience. The Datasets team owns the driving data that powers Wayve's AI Driver, managing ingestion, annotation, retrieval, and data quality across petabyte-scale datasets.
In this role, you will own the engineering systems that manage Wayve's massive and growing dataset infrastructure. Your day-to-day responsibilities include:
- Implementing and iterating filtering, retention, and offload policies across hot, cold, archive, and delete storage tiers
- Building data movement and pipelines that support pre-ingestion selection models
- Deciding where and how selection models run, balancing compute cost against data value
- Creating early, low-compute decision points that avoid expensive standardization and enrichment of low-value data
- Working closely with storage and data infrastructure teams to monitor data quality and ensure datasets reflect model training needs
Key projects include turning dataset strategy into working systems for moving, filtering, retaining, and offloading driving data at hundreds of petabytes; engineering the pre-ingestion selection pipeline; delivering measurable storage-cost savings through early filtering decisions; and ensuring datasets contain the most useful behavioral and environmental coverage for training.
You will work as a pair with the dataset strategy lead (who designs and builds the selection models) and closely with the data infrastructure team, shipping practical solutions quickly and iterating on live systems.
Wayve operates hybrid working from main hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver, and Leonberg, combining in-person collaboration with remote flexibility.
REQUIREMENTS:
- Senior data or software engineer with experience running data ingestion or data quality pipelines at petabyte scale
- Strong proficiency in Python and Spark with cloud data engineering experience
- Good judgment on data filtering and quality; ability to verify that valuable data is not being discarded
- Ability to ship practical solutions quickly and iterate on live systems
- Software engineer mindset, building systems around models rather than models themselves
- Comfortable working closely with data infrastructure and storage teams
- Bonus: experience in autonomous vehicles, robotics, fleet data, high-volume IoT, or with retention, offload, and data lifecycle policies
