We are looking for someone who can lead the development of ML-based algorithms that estimate driver
workload (busyness) by combining vehicle CAN signals, various in-vehicle sensors, and driver operation logs
as time-series data.
We welcome candidates who can leverage their expertise in driver state estimation, vehicle dynamics, sensor fusion, and machine learning to design and refine robust driver workload indices, while balancing them with other workload indicators such as surrounding-vehicle and vision-based measures.
We envision someone who is comfortable working in a high-uncertainty domain, iteratively testing hypotheses
with data, and collaborating with stakeholders to translate technical outcomes into product value.