The Role

At Wayve, we are building AI that learns to drive from experience. We do not hand-code cars to drive. We train our models from data gathered across the real world, making our ability to identify, understand and learn from the right data fundamental to everything we build.

We are looking for a VP of Data to lead the data strategy behind our autonomous driving technology.

This is a senior engineering leadership role with end-to-end ownership of how Wayve uses data to improve our AI. You will determine what data we need, how we acquire it, how we understand and enrich it, and ultimately how we turn it into measurable improvements in model performance.

The scale and shape of the problem is unique. Our data comes from our own vehicles, OEM programmes, partners and external sources, spanning different vehicles, sensors, markets and driving environments. Within that corpus, the most valuable examples are often the hardest to find: rare behaviours, unusual interactions, safety-critical events and the long tail of real-world driving.

Your job is not simply to build the systems that process this data. It is to decide what data matters and why, and build the strategy, organisation and learning loop that allows Wayve to learn from it faster.

You will lead Wayve’s Data organisation and provide senior ownership for data within Engineering, partnering closely across AI, Science, Validation, Product, Compute and our commercial teams.

Key Responsibilities

• Own Wayve’s data strategy: Set the strategy across the complete AI learning cycle, determining what data we need and how it should drive model performance, from foundation model training through specialised driving capabilities and production programmes.
• Build the data flywheel: Own the learning loop from real-world signals and model behaviour through data discovery, curation and enrichment into training, evaluation, deployment and measurement.
• Find the data that matters: Develop intelligent approaches to discovering rare, surprising and safety-critical scenarios within extremely large datasets, using techniques such as embeddings, semantic search, learned representations and model-driven data selection.
• Lead data acquisition and enrichment: Define how Wayve collects, buys, generates and enriches data across our own fleet, OEM programmes, partners and external sources, balancing coverage, quality, speed and economics.
• Connect data investment to model performance: Establish how we measure the value of data and make rigorous trade-offs across quality, accuracy, speed, cost and scale, ensuring investment is focused where it creates the greatest impact.
• Scale our data strategy globally: Build an approach that supports multiple OEMs, vehicle platforms, sensor configurations, geographies and regulatory environments while maintaining a scalable global architecture.
• Build and lead the organisation: Lead and develop a high-performing, multidisciplinary Data organisation, partnering closely with AI, Science, Validation, Product, Compute, Finance and Commercial teams.