Data Engineer, Application Software

As a Data Engineer at Wayve, you design scalable data pipelines to turn diverse vehicle data into reliable, model-ready datasets. You will support ML workflows from ingestion to production, collaborating with ML engineers and data teams. The role focuses on data quality, transformation, and pipeline efficiency to accelerate autonomous driving development. You help build a data foundation that enables scenario diversity and robust model evaluation. This is a hands-on role in a fast-evolving AI-driven mobility company.

• hybrid working
• relocation support and visa sponsorship
• learning and development budget
• comprehensive health benefits
• pension/retirement plan
• wellbeing partnerships and team socials

• Build and improve scalable data pipelines supporting model development and ML workflows
• Ingest, transform, and curate large-scale real-world, synthetic, and partner data into structured formats
• Develop data quality checks, validation, and monitoring for ML use cases
• Curate and mine data to enhance scenario diversity and feature development
• Improve pipeline performance, reliability, and usability to speed up ML iterations
• Collaborate with ML engineers, Data Corpus, AI Platform, and external partners to integrate pipelines with production systems

• Proven experience building and operating scalable data pipelines or distributed data processing in production
• Strong Python software engineering skills with maintainable and well-tested practices
• SQL and PySpark proficiency, familiarity with warehouse/OLAP concepts and Spark
• Experience with modern data pipeline architectures and DAG-based workflow tools (Airflow, Flyte, Ray or similar)
• Solid understanding of robotics/automated driving data concepts (sensor characteristics, timestamping, coordinate transforms, calibration, ego-motion signals)
• Understanding of ML development workflows (training data, evaluation datasets, scenario mining, model iteration)
• Excellent communication and collaboration across interdisciplinary teams

• strong communication
• collaboration
• problem-solving
• Python
• SQL
• PySpark