Staff Machine Learning Engineer, Data Curation & Dataset Strategy

Staff Machine Learning Engineer, Data Curation & Dataset Strategy

Wayve is building an artificial intelligence platform designed to enable autonomous vehicles to learn and adapt from real-world driving experience. The company has developed an end-to-end AI approach that allows vehicles to navigate complex urban environments without relying on pre-mapped routes or hand-coded rules. With operations across major hubs including London, Sunnyvale, Yokohama, Herzliya, Vancouver and Leonberg, Wayve is advancing the autonomous driving industry through scalable, generalizable intelligence that can power vehicles across different manufacturers and geographies.

This role sits within Wayve's Simulation, Evaluation, and Validation team and focuses on the critical challenge of managing driving data at massive scale. As datasets grow from hundreds of petabytes toward exabyte scale, decisions about what data to retain, archive, or discard become central levers for both model performance and operational cost. You will shape the dataset strategy that model engineers depend on across the company.

What you'll do

• Own the data scoring and routing system that evaluates incoming driving data and directs it to hot storage, cold storage, archive, or deletion based on strategic criteria
• Partner with model training teams to establish the correct data distributions needed for each model in development across the company
• Define dataset content strategy by identifying which data and metadata are essential, allowing training teams to select the most valuable information efficiently
• Translate model engineering needs into data selection priorities, ensuring that critical scenarios (such as underperforming edge cases) are prioritized for hot storage
• Develop increasingly sophisticated filtering approaches, progressing from practical heuristics to machine learning-driven methods, and guide filtering decisions at vehicle and edge levels
• Work with ingestion, annotation, retrieval, data quality, model training, evaluation, and storage teams to establish company-wide data policies
• Lead a small team of software and machine learning engineers in building data provisioning and provenance features into the Corpus system

What you'll bring

• Substantial experience in autonomous vehicles or robotics, working with large-scale driving data, sensor data, or comparable datasets
• Proven track record as a hands-on machine learning engineer who has built models for data scoring, selection, or filtering
• Deep familiarity with petabyte-scale data operations and the engineering practices required at that scale
• Strong proficiency in Python and Spark with substantial cloud data platform experience
• Excellent quantitative reasoning skills that inform practical, measurable business decisions
• High ownership mentality and pragmatic approach to setting direction across multiple teams without formal authority
• Comfort working across functional boundaries to influence data strategy and implementation

Nice to have

• Experience with data selection, retention, or filtering strategies for model training
• Background in edge or vehicle-side filtering systems
• Computer vision or high-volume IoT data experience
• Track record leading small cross-functional teams

What they offer

• Salaries benchmarked annually against the market
• Meaningful equity reflecting long-term ownership in Wayve's success
• Relocation support and visa sponsorship where applicable
• Hybrid working arrangement with core hours and access to vehicle workshops and labs
• Learning and development budgets for training, conferences, and professional growth