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Wayve is building the leading AI platform for autonomous driving using an end-to-end learning approach. This Staff-level ML engineering role sits at the center of Wayve's data strategy, owning the datasets that power the company's AI Driver across ingestion, annotation, retrieval, and data quality.
You will lead the data curation and dataset strategy function as the company scales from hundreds of petabytes toward exabyte-scale data. Your core responsibilities include:
**Day-to-Day Work:**
- Build and own the ingestion-time model that scores incoming driving data and routes it to hot, cold, archive, or delete storage tiers
- Work with training teams to define optimal data distributions for every model in development
- Deliver measurable storage-cost savings through practical heuristics, then evolve to ML-driven filtering approaches
- Partner across ingestion, annotation, retrieval, data quality, model training, AV/robotics, evaluation, and storage teams to establish company-wide data policy
- Lead and work hands-on with a small group of software and ML engineers to build data provisioning and data provenance features into Corpus (Wayve's internal data platform)
**Strategic Focus Areas:**
- Define dataset content strategy: determining which data and metadata the company needs and why, enabling training teams to select the most valuable data efficiently
- Steer data selection based on model engineer needs in real time (e.g., prioritizing underrepresented scenarios like unprotected right turns at traffic lights to hot storage)
- Deliver large, measurable storage-cost savings as ingestion scales across Wayve's internal fleet, partner data, and OEM integrations
- Evolve filtering from practical heuristics to ML-driven systems, influencing what gets filtered at the vehicle and edge
- Pair with the Corpus Implementation Engineer to translate strategy into production systems
Wayve operates a hybrid model with main hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver, and Leonberg. You'll have the opportunity to work hands-on in vehicle workshops and labs alongside focused remote work time.
**Requirements:**
- Strong autonomous-vehicle or robotics experience, working with large-scale driving, sensor, or comparable data
- Principal-level, hands-on ML engineer who has built models that score, select, or filter data
- Demonstrated experience operating at petabyte scale and comfort with associated engineering challenges
- Strong proficiency in Python and Spark, with large-scale cloud data experience
- Strong quantitative intuition and ability to make practical, measurable decisions
- Pragmatic, high-ownership mindset; ability to set direction across multiple teams without direct authority
- Bonus experience in: data selection/retention/filtering for model training, edge or vehicle-side filtering, computer vision, high-volume IoT data, or leading small cross-functional groups
Wayve notes that not meeting every requirement is acceptable if you are passionate about autonomy and eager to learn.
