AI Engineer, Driver Workload Estimation

RESPONSIBILITIES

• 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

MINIMUM QUALIFICATIONS

• 3+ years of practical experience in software or algorithm development primarily using vehicle CAN signals, in-vehicle sensors, and driver operation logs as time-series data
• Practical experience developing algorithms using machine learning, deep learning, and/or statistical modeling
• Development experience in Python using major ML/DL frameworks (e.g., PyTorch, TensorFlow)
• Ability to independently drive the end-to-end ML development process from data preprocessing and feature design through training and evaluation
• Communication skills to work with multiple stakeholders and clearly explain technical topics and evaluation results
• Willingness to travel for business purposes as required by project or business needs
• Business level Japanese proficiency and conversational level English

NICE TO HAVES

• Ability and willingness to work at the Susono office
• Development or evaluation experience in domains such as in-vehicle systems, driver assistance (ADAS), driver monitoring, or robotics
• Experience in feature engineering and developing driver workload or driver state estimation algorithms using vehicle CAN signals, various in-vehicle sensors, and driver operation logs as time-series data
• Experience deploying ML models to production, including edge/embedded optimization, inference pipeline construction, and model operations
• Experience using simulators (e.g., Unity) for algorithm evaluation or data generation
• External outputs in ML, signal processing, or related fields, such as competition results or academic publications
• Bachelor’s degree in computer science, electrical/electronic engineering, control engineering, or a related field, or equivalent practical experience

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Important Points
・All interviews will be arranged via Google Meet, unless otherwise stated.
・The same job descriptions are available in both English and Japanese; therefore, we kindly ask that you apply to only one version.
・We kindly request that you submit your resume in English, if possible. However, Japanese resumes are also acceptable. Please note that, depending on the English proficiency requirements of the role, we may request an English version of your resume later in the process.

WHAT WE OFFER
・Competitive Salary - Based on experience
・Work Hours - Flexible working time
・Paid Holiday - 20 days per year (prorated)
・Sick Leave - 6 days per year (prorated)
・Holiday - Sat & Sun, Japanese National Holidays, and other days defined by our company
・Japanese Social Insurance - Health Insurance, Pension, Workers’ Comp, and Unemployment Insurance, Long-term care insurance
・Housing Allowance
・Retirement Benefits
・Rental Cars Support
・In-house Training Program (software study/language study)

Our Commitment
・We are an equal opportunity employer and value diversity.
・Any information we receive from you will be used only in the hiring and onboarding process. Please see our privacy notice for more details.