AI Engineer

• Design, implement, and enhance ML-based driver workload (busyness) estimation algorithms that take vehicle CAN signals, various in-vehicle sensors, and driver operation logs as time-series inputs
• Build and operate training data infrastructure and evaluation pipelines, including label and annotation policy design, data preprocessing, and feature engineering
• Investigate and improve logics that combine CAN-based workload indicators with other workload measures, such as surrounding-vehicle information and image/vision-based indicators
• Design interfaces and specifications to feed workload estimation results into driving suggestion and voice prompt logic, and continuously improve overall feature performance
• Collaborate with software engineers, test engineers, UX members, and other stakeholders to align on requirements and evaluation metrics, and to organize and share experiment results and technical learnings
• Participate, as required by projects and business needs, in planning and conducting evaluations using simulators and on-road test vehicles (including business trips), and drive improvement cycles based on real-world findings

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Driver Workload Estimation AI Engineer, Info Mobility