Vice President, AI Data

Overview

As VP of Data, you will own Wayve’s data strategy across the full AI learning cycle to boost model performance. You’ll lead a multidisciplinary data organization and align data initiatives with Engineering, AI, Science, Validation, Product, Compute and Commercial teams. Your work focuses on identifying valuable data, sourcing it, and turning it into measurable improvements for the AI Driver. This is a high-impact leadership role shaping data-driven breakthroughs at scale.

Pay / Benefits

• salary benchmarked against market
• meaningful equity
• relocation support and visa sponsorship
• hybrid working with office and remote flexibility
• learning and development support
• comprehensive location-dependent benefits (health, dental, maternity/paternity leave, retirement, wellbeing)

Responsibilities

• Define and execute data strategy across the AI learning cycle to maximize model capability and performance
• Own the data flywheel from real-world signals to training, evaluation, deployment and measurement
• Develop intelligent approaches to discovering rare, safety-critical scenarios in large datasets using embeddings, semantic search and learned representations
• Lead data acquisition and enrichment across our fleet, OEMs, partners and external sources balancing coverage, quality, speed and cost
• Establish metrics to value data and optimize trade-offs across quality, speed, cost and scale
• Scale data strategy globally to support multiple OEMs, vehicle platforms, sensor setups, geographies and regulatory environments
• Build and lead a high-performing, multidisciplinary Data organization spanning research, engineering and commercial programs

Key requirements

• Senior engineering or technical leadership experience with high-performing technical orgs
• Experience at the intersection of AI, data and large-scale model development influencing training and performance
• Deep technical expertise in computer vision, video, multimodal AI, perception or related ML problems
• Strong intuition for data value and experience with discovery, selection, curation and enrichment at scale
• Ability to translate strategy into deep technical work and dataset-level analysis
• Strong technical and commercial judgement with trade-offs across quality, speed, cost and scale
• Comfort operating in ambiguity and influencing senior stakeholders across functions
• Experience in autonomous driving, robotics or embodied AI is valuable
• Exposure to large-scale video or multimodal foundation model training, semantic search, embeddings, active learning, auto-labelling or real-world data acquisition

• strategic thinking
• stakeholder management
• cross-functional collaboration
• data strategy for AI learning cycles
• data discovery, curation and enrichment at scale
• embeddings and semantic search

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