Data Engineer, Application Software

Highlights

• Design pipelines turning vast vehicle data into model-ready datasets.
• Curate real-world and synthetic data to drive scenario diversity.
• Support core driving, personalisation, comfort and collaboration features.

Ideal candidate

A collaborative data engineer passionate about autonomous driving who thrives in fast-moving, ownership-driven environments and enjoys turning messy real-world vehicle data into reliable foundations for ML.

Requirements

• Proven experience building and operating scalable data pipelines or distributed data processing systems in production environments.
• Strong software engineering skills in Python, with a solid foundation in maintainable, reliable, and well-tested software development practices.
• Proficient in SQL and PySpark, with experience using warehouse/OLAP concepts, window functions, and Spark for distributed data processing.
• Experience with modern data pipeline architectures, including workflow orchestration and DAG-based systems such as Airflow, Flyte, Ray, or similar.
• Solid understanding of robotics and automated driving data concepts, including sensor characteristics, timestamping and clock synchronisation, coordinate transformations, calibration, and ego-motion signals such as GNSS/IMU and vehicle odometry.
• Understanding of machine learning development workflows, including training data generation, evaluation datasets, scenario mining, and model iteration.
• Excellent communication and collaborative skills, capable of working effectively with interdisciplinary teams.
• Prior work in calibration, perception, imitation learning, or trajectory prediction.
• Experience with third-party dataset ingestion and transformation.
• Familiarity with automated driving data and scenario taxonomies, including ODD definitions, manoeuvre and behavior labels, scene classification, event tagging, semantic understanding.