Vice President, AI Data

Overview

In this role you lead Wayve’s data strategy to power autonomous driving AI. You own the end-to-end data lifecycle, from what data to collect to how it improves model performance, across a global operating model with OEMs, partners and diverse environments. You build and scale a data organisation that enables faster learning from real-world signals. You collaborate across AI, Science, Validation, Product, Compute and Commercial teams to drive measurable model improvements. This is a high-impact leadership opportunity at a fast-moving, mission-driven company.

Pay / Benefits

• hybrid working policy
• office locations in Sunnyvale or London

Responsibilities

• Own Wayve’s data strategy across the full AI learning cycle to drive model performance
• Build the data flywheel by turning real-world signals into training, evaluation, deployment and measurement
• Discover data that matters by finding rare, safety-critical scenarios using embeddings, semantic search and learned representations
• Lead data acquisition and enrichment across own fleet, OEM programmes, partners and external sources
• Connect data investment to model performance through rigorous value measurement and cost-quality trade-offs
• Scale the data strategy globally to support multiple OEMs, platforms, sensors, geographies and regulatory environments
• Build and lead a multidisciplinary Data organisation coordinating with AI, Science, Validation, Product, Compute, Finance and Commercial teams

Key requirements

• Significant engineering leadership experience with high-performing technical organisations
• Deep technical expertise in computer vision, video, multimodal AI or related perception problems
• Experience leading large-scale data capabilities that influence model training, evaluation and performance
• Strong intuition for data value with experience in discovery, curation and enrichment at scale
• Strong technical and commercial judgement including trade-offs across quality, speed, cost, scale and make-or-buy decisions
• Ability to operate at strategy-to-technical-detail level and influence senior stakeholders

• Strategic thinking in ambiguity
• Strong technical judgment and cross-functional influence
• Communication and leadership across diverse teams
• Data strategy for AI learning cycles
• Data discovery, curation and enrichment at scale
• Embeddings, semantic search and learned representations

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