About The Role

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