Staff Machine Learning Engineer, Data Curation & Dataset Strategy

Wayve • Sunnyvale, CA

22h • Hybrid

About The Position

The Staff Machine Learning Engineer, Data Curation & Dataset Strategy will shape the driving data used to train Wayve’s AI Driver. The role focuses on deciding which data to retain and where to store it as datasets grow from hundreds of petabytes toward exabyte scale, balancing model quality and storage cost. It combines hands-on machine learning and engineering with strategic work across teams responsible for data ingestion, annotation, retrieval, data quality, model training, autonomous vehicles and robotics, evaluation, and storage.

The engineer will build and own a model that scores incoming driving data at ingestion and routes it to hot, cold, archive, or deletion. They will work with training teams to define useful data distributions, deliver measurable storage savings, and evolve practical filtering heuristics into ML-driven approaches. They will also lead and work hands-on with a small group of software and ML engineers to develop data provisioning and provenance features in Corpus, partnering with the Corpus Implementation Engineer to bring strategy into production.

Wayve operates a hybrid working model, combining collaboration in dedicated offices with remote work. Its main hubs are London, Sunnyvale, Yokohama, Herzliya, Vancouver, and Leonberg. The company describes its environment as one where ways of working are still being developed, offering ownership for people who want to help shape them. Relocation support and visa sponsorship may be available where applicable.

Requirements

• Strong autonomous-vehicle or robotics experience working with large-scale driving, sensor, or comparable data.
• Principal-level, hands-on machine learning engineering experience, including building models that score, select, or filter data.
• Experience operating at petabyte scale and comfort with the associated engineering challenges.
• Strong Python and Spark skills, with experience in large-scale cloud data.
• Strong quantitative intuition and the ability to make practical, measurable decisions.
• Pragmatic, high-ownership approach and ability to set direction across several teams without direct authority.

Nice To Haves

• Experience in data selection, retention, or filtering for model training.
• Experience with edge or vehicle-side filtering.
• Computer vision experience.
• Experience with high-volume IoT data.
• Experience leading small cross-functional groups.

Responsibilities

• Build and own an ingestion-time model that scores incoming driving data and routes it to hot, cold, archive, or delete.
• Work with training teams to define the right data distributions for the models being developed.
• Deliver practical, measurable storage-cost savings, beginning with heuristics and evolving toward ML-driven filtering.
• Partner with 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 in Corpus.
• Define dataset content strategy, including which data and metadata are needed and why.
• Guide data selection according to model-engineering needs, prioritizing useful scenarios for hot storage.
• Influence filtering at the vehicle and edge as data-selection approaches evolve.
• Work with the Corpus Implementation Engineer to turn strategy into production systems.

Benefits

• Salaries benchmarked against the market annually.
• Meaningful equity.
• Relocation support and visa sponsorship where applicable.
• Hybrid working, core hours, and the opportunity to work hands-on in vehicle workshops and labs.
• Learning and development budgets, with support for training, conferences, and growth.
• Comprehensive benefits, including health insurance, dental coverage, enhanced maternity and paternity leave, retirement or pension where applicable, access to therapists, wellbeing partnerships, and team socials.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed