Vice President, AI Data

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 Teams At Wayve, we are building AI that learns to drive from experience. We do not hand-code cars to drive. Our models learn from data gathered across the real world, making our ability to identify, understand and learn from the right data fundamental to everything we build. Our Data organisation sits at the heart of this learning loop. It works across AI, Science, Validation, Product, Compute and our commercial programmes to determine what data Wayve needs, how we acquire and understand it, and how we turn it into measurable improvements in our AI Driver. 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. We are looking for a VP of Data to lead this organisation and own the data strategy behind Wayve’s autonomous driving technology. 🧠 Your day-to-day As VP of Data, you will have end-to-end ownership of how Wayve uses data to improve our AI. You will 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. You will lead a multidisciplinary Data organisation and work closely with senior leaders across Engineering, AI, Science, Validation, Product, Compute, Finance and Commercial teams. This is not simply about building systems that process data. You will be responsible for deciding what data matters and why, making significant technical and investment decisions, and building the strategy, organisation and learning loop that enables Wayve to learn from the real world faster. 🧩 What you’ll be working on: - Owning Wayve’s data strategy: Set the strategy across the complete AI learning cycle and determine where data can have the greatest impact on model capability and performance. - Building 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. - Finding 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. - Leading 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. - Connecting 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. - Scaling 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. - Building and leading the organisation: Develop a high-performing, multidisciplinary Data organisation capable of operating across Wayve’s research, engineering and commercial programmes. 🙌 You should apply if: - You are a senior engineering or technical leader with significant experience building and leading high-performing technical organisations. - You have operated at the intersection of AI, data and large-scale model development, with data capabilities that directly influence model training, evaluation and performance. - You have deep technical expertise in areas such as computer vision, video, multimodal AI, perception or related large-scale ML problems. - You have strong intuition for what makes data valuable and experience with data discovery, selection, curation and enrichment at scale. - You can move comfortably between strategy and technical detail, from defining an organisation-wide data strategy to going deep on a dataset, model behaviour or learning loop. - You have strong technical and commercial judgement and have made meaningful trade-offs across quality, speed, cost, scale and build-versus-partner decisions. - You are comfortable setting direction in ambiguity and influencing senior technical and business stakeholders across organisational boundaries. Experience in autonomous driving,