Principal Data Engineer, Data Quality & Datasets Pipelines

Wayve's Datasets team owns the driving data that the AI Driver learns from, covering ingestion, annotation (automatic and manual), retrieval and data quality. The data is growing from hundreds of petabytes towards exabyte scale, and the team decides what to keep and where, and builds the systems that make those decisions work in practice. Wayve is building the leading AI platform for autonomous driving, using an end-to-end AI approach that enables vehicles to learn directly from real-world experience.

Responsibilities

Implement and iterate filtering, retention and offload policies across hot, cold, archive and delete storage; Build the data movement and pipelines that the pre-ingestion selection model depends on; Decide where and how selection models run, balancing compute cost against data value; Build early, low-compute decision points that avoid expensive standardisation and enrichment of low-value data; Work with the storage and data infra teams, and monitor data quality to make sure datasets reflect what model training needs; Turn the dataset strategy into working systems for moving, filtering, retaining and offloading driving data at hundreds of petabytes; Deliver large, measurable storage-cost savings through early, low-compute decisions; Ensure datasets contain the most useful behavioural and environmental coverage for training; Work as a pair with the dataset strategy lead and closely with the data infra team, shipping practical solutions quickly

Requirements

Senior data or software engineer who has run data ingestion or data quality pipelines at petabyte scale; Strong in Python and Spark, with cloud data engineering experience; Good judgement on data filtering and quality, and knows how to check that valuable data is not discarded; Ships practical solutions quickly and is comfortable iterating on live systems; Software engineer at heart, building the systems around models rather than the models themselves; Enjoys working closely with data infra and storage teams

Nice to have

Experience in AV, robotics, fleet data or high-volume IoT; Experience with retention, offload and data lifecycle policies

Benefits

Salaries benchmarked against the market annually; Meaningful equity; Relocation support and visa sponsorship where applicable; Hybrid working, core hours and the chance to work hands on in vehicle workshops and labs; Learning and development budgets with support for training, conferences and growth; Comprehensive benefits including health insurance, dental, enhanced maternity and paternity leave, retirement or pension where applicable, access to therapists, wellbeing partner discounts and more