ML Algorithm Developer, Driver Workload Estimation at Woven by Toyota

Department: Woven City

Language

Setup: Hybrid
Location: Tokyo
Level: mid
Posted: Apr 27, 2026

Description

Toyota is hiring an ML Algorithm Developer for its Info Mobility Car team to lead the development of machine-learning-based driver workload (busyness) estimation algorithms. The role combines vehicle CAN signals, in-vehicle sensors, and driver operation logs into time-series data, builds training and evaluation infrastructure, refines workload indicators, and integrates results into driving suggestions and voice prompts. Candidates need at least three years of software or algorithm development experience, machine learning or statistical modeling experience, Python and major ML/DL framework experience, and the ability to work with stakeholders and travel for business.

Required Skills

Machine learning
Vehicle CAN
Sensor fusion
Python
PyTorch
Feature engineering
Driver state estimation

Additional Information

Required Previous Experiences: 3+ years of software or algorithm development using vehicle CAN signals, in-vehicle sensors, and driver operation logs as time-series data
Experience developing algorithms using machine learning, deep learning, and/or statistical modeling
Python development using major machine learning or deep learning frameworks such as PyTorch or TensorFlow
End-to-end machine learning development from data preprocessing and feature design through training and evaluation
Preferred Qualifications: Ability and willingness to work at the Susono office
Experience in in-vehicle systems, driver assistance, driver monitoring, or robotics
Experience developing driver workload or driver state estimation algorithms using vehicle CAN signals and in-vehicle sensors
Experience deploying machine learning models to production, including edge optimization and inference pipelines
Experience using simulators such as Unity for algorithm evaluation or data generation
External outputs in machine learning, signal processing, or related fields
Bachelor’s degree in computer science, electrical or electronic engineering, control engineering, or a related field, or equivalent practical experience