Driver Workload Estimation AI Engineer, Info Mobility Car
On-site · Tokyo, Tokyo, Japan or Susono, Shizuoka, Japan
Job Summary
Lead the development of ML-based driver workload (busyness) estimation algorithms by combining vehicle CAN signals, in-vehicle sensors, and driver operation logs as time-series data. Build and operate training data infrastructure and evaluation pipelines, including label/annotation policy design, data preprocessing, and feature engineering. Improve logics that fuse CAN-based workload indicators with other workload measures (surrounding-vehicle and vision-based indicators). Design interfaces to feed workload estimates into driving suggestion and voice-prompt logic, and drive overall feature performance. Collaborate with software engineers, test engineers, UX members, and stakeholders to align requirements and evaluation metrics, and share experimental learnings. Participate in simulator and on-road evaluations as needed, and drive iterative improvement cycles based on real-world findings.
Required Qualifications
- 3+ years of practical experience in software or algorithm development using vehicle CAN signals, in-vehicle sensors, and driver operation logs as time-series data
- Experience developing ML, deep learning, and/or statistical modeling algorithms
- Development experience in Python with ML/DL frameworks (PyTorch, TensorFlow)
- Ability to independently drive end-to-end ML development from data preprocessing to training and evaluation
- Strong communication skills to work with multiple stakeholders and explain technical results
- Willingness to travel for business purposes
- Business level Japanese proficiency and conversational English
- Bachelor’s degree in computer science, electrical/electronic engineering, control engineering, or a related field, or equivalent practical experience
Additional Requirements
- Willingness to travel for business purposes
- Business level Japanese and conversational English
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