Design, implement, and enhance ML-based driver workload estimation algorithms using vehicle CAN signals, in-vehicle sensors, and driver operation logs as time-series inputs
Build and operate training data infrastructure and evaluation pipelines, including label/annotation policy design, data preprocessing, and feature engineering
Investigate and improve logic that combines CAN-based workload indicators with other measures (surrounding-vehicle and vision-based indicators)
Design interfaces to feed workload estimation results into driving-suggestion and voice-prompt logic; continuously improve overall feature performance
Collaborate with software, test, UX, and other stakeholders to align requirements and evaluation metrics, and share experiment results and learnings
Participate in planning and conducting evaluations using simulators and on-road test vehicles, driving improvement cycles from real-world findings
技術スタック
必須スキル
3+ years of practical experience in software/algorithm development using vehicle CAN signals, in-vehicle sensors, and driver operation logs as time-series data
Practical experience with machine learning, deep learning, and/or statistical modeling
Python development with ML/DL frameworks (e.g., PyTorch, TensorFlow)
Ability to independently drive end-to-end ML development from data preprocessing and feature design through training and evaluation
Strong communication skills to work with multiple stakeholders
Willingness to travel for business purposes
Business-level Japanese and conversational English
歓迎スキル（該当する場合）
Ability and willingness to work at the Susono office
Experience in in-vehicle systems, ADAS, driver monitoring, or robotics
Experience in feature engineering and developing driver workload/state estimation using CAN signals, sensors, and time-series data
Experience deploying ML models to production (edge/embedded optimization, inference pipeline, MLOps)
Experience using simulators (e.g., Unity) for evaluation or data generation
External outputs in ML or signal processing (competitions, publications)
Bachelor’s degree in CS, electrical/electrical engineering, control engineering, or related field, or equivalent practical experience