Data Engineer

Job description

About us Founded in 2017, Wayve is the leading developer of Embodied AI technology. Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems. Our vision is to create autonomy that propels the world forward. Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving. In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future. At Wayve, your contributions matter. We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact. Make Wayve the experience that defines your career! The Role As a Data Engineer within the Machine Learning team in Application Software, you’ll contribute to critical initiatives that push the frontier of model-based autonomous driving—both in terms of core driving performance and feature-level intelligence such as personalization, comfort, and collaboration. You’ll design and deliver scalable data pipelines that transform vast amounts of data from diverse internal and external sources into structured, reliable, and model-ready datasets. Your work will span data ingestion, data quality assurance, transformation, curation, evaluation and ML support. You’ll collaborate deeply with Wayve’s Data Corpus teams and ML engineers to build systems that are performant, adaptable, and ready for production. Key Responsibilities: Build and improve scalable data pipelines that support model development, evaluation, and production ML workflows for autonomous driving. Ingest, transform, and curate large-scale real-world, synthetic, and partner-provided datasets into structured, reliable, and model-ready formats aligned with standardised taxonomies and coordinate systems. Develop data quality checks, validation processes, and monitoring to ensure both raw data from our vehicle platforms and processed datasets are high-quality, complete, consistent, traceable, and fit for ML use cases. Curate and mine real-world and synthetic data to drive scenario dive…