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 data engineering frameworks, ETL workflows, and distributed processing systems. 

• Experience handling large-scale sensor data from cameras, radar, LiDAR, IMU, GNSS, or similar data sources. 

• Strong understanding of data quality management, data validation, and pipeline monitoring. 

• Experience with dataset versioning, reproducibility, and data lineage concepts. 

• Strong knowledge of Linux environments and software development best practices. 

• Experience with cloud (Azure, AWS, or similar) or distributed computing environments. 

• Strong analytical and problem-solving skills. 

• Excellent communication skills and ability to collaborate across multidisciplinary engineering teams. 

Preferred Qualifications 

• Experience in robotics, autonomous systems, autonomous vehicles, drones, AMRs, or related domains. 

• Experience with AI/ML data preparation, dataset curation, and training data management. 

• Familiarity with annotation workflows, ground-truth generation, and sensor calibration processes. 

• Experience with tools such as DVC, MLflow, Airflow, Spark, Kubernetes, or similar platforms. 

• Experience building data quality dashboards and analytics solutions using tools such as Grafana, Power BI, Tableau, Plotly, or equivalent. 

• Knowledge of data lake architectures and large-scale storage systems. 

• Experience with ROS/ROS2 and robotic data recording formats. 

• Experience supporting ML training workflows and model evaluation infrastructure. 

• Familiarity with MLOps and DataOps practices. 

• Experience working in fast-paced start-up or incubation environments. 

Traits We Seek 

• Systems Thinkers who understand how data flows through complex robotics and AI/ML ecosystems. 

• Ownership Mentality with a strong focus on reliability, quality, and operational excellence. 

• Automation Advocates who eliminate manual processes through scalable engineering solutions. 

• Data-Driven Problem Solvers who use metrics and evidence to drive improvements. 

• Collaborative Influencers who effectively work across software, ML, robotics, and infrastructure teams.