VP of Data for Autonomous Driving AI

Wayve; Greater London; Hybrid; GBP 180,000 - 240,000; Full time

Benefits offered by this job

Equity

Hybrid working

Relocation support

Learning & development

Health insurance

Job summary

Wayve is seeking a VP of Data to lead the data organisation and own the data strategy behind its autonomous driving technology. You will drive data acquisition, enrichment and measurement to improve AI Driver performance, working across engineering, science and product teams.

You will set the strategy for the entire AI learning cycle, from data collection to model evaluation and production deployment, balancing quality, cost and scale while guiding a multidisciplinary team across global hubs.

Qualifications

• Senior engineering or technical leadership with experience building and guiding large technical teams.
• Experience at AI, data and large-scale model development affecting training and performance.
• Deep expertise in computer vision, video, multimodal AI, perception or related ML problems.
• Proven ability to discover, curate and enrich data at scale and tie data value to model outcomes.
• Able to balance strategy with hands-on analysis of datasets and learning loops.

Responsibilities

• Own Wayve's data strategy across the AI learning cycle and model outcomes.
• Lead a multidisciplinary Data organisation spanning research, engineering and product.
• Define data acquisition, enrichment and measurement to optimise model performance.
• Scale data architectures globally across OEMs, platforms, geographies and regulations.
• Balance quality, speed, cost and scale in data investments and trade-offs.

Skills

Data strategy

Leadership

AI leadership

Computer vision

Multimodal AI

Data discovery

Dataset curation

Model performance

Job description

Wayve is seeking a VP of Data to lead the data organisation and own the data strategy behind its autonomous driving technology. You will drive data acquisition, enrichment and measurement to improve AI Driver performance, working across engineering, science and product teams.

You will set the strategy for the entire AI learning cycle, from data collection to model evaluation and production deployment, balancing quality, cost and scale while guiding a multidisciplinary team across global hubs.