Senior AI/ML Data Engineer – Robotics & Drones (f/m/d)

We are Aptiv - a global technology company with 200,000 specialists in 48 countries. We develop innovative software and build the hardware to bring autonomous driving cars, advanced driver-assistance systems, connected vehicles and smart cities to life in a way that only we can. As a Senior AI/ML Data Engineer, you will own the end-to-end data processing and dataset lifecycle required to develop, train, validate, and continuously improve AI/ML systems across our robotics platforms. You will ensure that raw sensor recordings are transformed into reliable, high-quality training and test datasets through robust, scalable, and automated data pipelines. You will work closely with AI/ML engineers, perception engineers, validation teams, and platform engineers to ensure that data is available, trustworthy, traceable, and ready for model development and performance evaluation. Key Responsibilities Data Pipeline Architecture & Automation Design, develop, and maintain automated data processing pipelines that transform raw robotic sensor recordings into ML-ready datasets. Establish scalable and reproducible workflows supporting the complete AI/ML development lifecycle. Drive continuous improvements in pipeline reliability, scalability, maintainability, and performance. Dataset Engineering & Lifecycle Management Own the lifecycle management of datasets used for AI/ML model development, validation, and benchmarking. Define and automate dataset creation processes for model training, model validation, model benchmarking, and regression testing. Ensure dataset traceability, reproducibility, and version control. Data Quality & Validation Define and implement automated quality checks throughout the data processing chain. Verify data correctness, completeness, consistency, and integrity after every processing step. Identify data quality issues and drive corrective actions with stakeholders. Data Distribution & Infrastructure Integration Manage distribution of datasets across file systems, cloud environments, and training infrastructure. Optimize large-scale dataset storage, transfer, and access mechanisms. Support compute platforms used for AI/ML training and evaluation. Metrics, Reporting & Visualization Develop dashboards and reporting solutions to monitor: Data KPI including data size, growth, and quality Coverage of operational scenarios Label and ground-truth quality AI/ML readiness KPIs Basic Qualifications Master's degree in Computer Science, Data Engineering, Robotics, Software Engineering, Electrical Engineering, or a related technical field, or equivalent practical experience. 5+ years of experience developing large-scale data processing systems, data pipelines, or ML data infrastructure. Strong proficiency in Python and experience building production-quality software. Experience with ...