Principal Data Engineer, Data Quality & Datasets Pipelines

Before the detail, here's the challenge you'd help us solve. We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that. Here’s what this particular role covers. 🛠️ About our Datasets Team Wayve’s Datasets team own the driving data that our AI Driver learns from, covering ingestion, annotation (automatic and manual), retrieval and data quality. Our data is growing from hundreds of petabytes towards exabyte scale, and not all of it is equally valuable for training. This team decides what we keep and where, and builds the systems that make those decisions work in practice.   🧠 Your day-to-day Implement and iterate filtering, retention and offload policies across hot, cold, archive and delete storage Build the data movement and pipelines that our 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 our datasets reflect what model training needs   🧩 What you’ll be working on: Turning our dataset strategy into working systems: moving, filtering, retaining and offloading driving data at hundreds of petabytes The engineering behind our pre-ingestion selection mode(s)l: how data moves, where the model runs and how offload policies work Delivering large, measurable storage-cost savings through early, low-compute decisions Making sure our datasets contain the most useful behavioural and environmental coverage for training. This is as much a data quality challenge as a software engineering one Working as a pair with our dataset strategy lead (who design and builds the models) and closely with the data infra team, shipping practical solutions quickly   👤 You should apply if: You’re a senior data or software engineer who has run data ingestion or data quality pipelines at petabyte scale You’re strong in Python and Spark, with cloud data engineering experience You have good judgement on data filtering and quality, and know how to check you haven’t discarded valuable data You ship practical solutions quickly and are comfortable iterating on live systems You’re a software engineer at heart, building the systems around models rather than the models themselves You enjoy working closely with data infra and storage teams You bring experience in AV, robotics, fleet data or high-volume IoT, or with retention, offload and data lifecycle policies (a bonus rather than a requirement)   🌱 Not ticking every box? That’s totally okay! If you’re passionate about autonomy and keen to learn, we encourage you to apply even if you don’t meet every requirement.   More about Wayve: 🚗 Wayve is building the leading AI platform for autonomous driving. We are pioneering an end to end AI approach that enables vehicles to learn directly from real world experience, developing the ability to adapt, generalise and improve at scale. Instead of relying on hand coded rules or pre mapped environments, our AI Driver learns to drive by understanding the world around it. The result is technology that navigates complex urban environments with intelligence, precision and natural flow, unlocking meaningful advances in both safety and efficiency. We believe autonomy represents a once in a generation transformation in how people and goods move, comparable to the shift from horses to cars, and from human driven vehicles to intelligent machines. Our ambition is to make autonomy universal. Wayve’s mapless and hardware agnostic AI platform integrates with global OEM partners, enabling continuous software evolution and unlocking advanced levels of automation from L2 plus through to L4 as our core AI model scales. In a race