Vice President AI Data
Wayve is a London-headquartered embodied-AI company developing and licensing mapless, vehicle-agnostic driving software for assisted, automated, and robotaxi applications.
About Wayve
Wayve Technologies Ltd. develops the Wayve AI Driver, an end-to-end, data-trained software platform that runs on onboard vehicle compute and native sensors. It is designed for OEM integration across L1 driver assistance through L4 automated driving, without HD maps.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will set the data strategy across the AI learning cycle and lead data acquisition, discovery, curation, enrichment, and measurement. You will build the data flywheel, identify high-value scenarios, connect investment to model performance, and lead a multidisciplinary data organization.
Requirements
- Engineering leadership experience building and leading high-performing technical organizations
- Technical expertise in computer vision, video, multimodal AI, or related perception problems
- Experience leading large-scale data capabilities influencing model training, evaluation, and performance
- Experience with data discovery, selection, curation, and enrichment at scale
- Technical and commercial judgment across quality, speed, cost, scale, and build-versus-partner decisions
- Ability to set direction in ambiguity and influence senior technical and business stakeholders
- Experience with autonomous driving, robotics, or embodied AI
- Experience with large-scale video, multimodal, or foundation-model training
- Experience with semantic search, embeddings, active learning, auto-labelling, or intelligent data selection
- Experience with large-scale real-world data acquisition
Responsibilities
- Set data strategy across the complete AI learning cycle
- Build the learning loop from real-world signals through training, evaluation, deployment, and measurement
- Develop approaches to discover rare and safety-critical scenarios
- Lead data acquisition and enrichment across fleets, OEM programs, partners, and external sources
- Measure data value and make trade-offs across quality, accuracy, speed, cost, and scale
- Build a global data strategy across vehicle platforms, sensors, geographies, and regulatory environments
- Lead and develop a multidisciplinary data organization
Benefits
- Hybrid working arrangement