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Woven

Develops global intelligent mobility solutions

AI Engineer: Driver Workload Estimation

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeNo salary listed

About the Job

Requirements

  • At least 3 years of professional experience in software or algorithm development, primarily using vehicle CAN signals, in-vehicle sensors, driver operation logs, or other time-series data.
  • Professional experience developing algorithms using machine learning, deep learning, or statistical modeling, including development with Python and major machine-learning or deep-learning frameworks such as PyTorch or TensorFlow.
  • Ability to independently carry out the full machine-learning development process, from data preprocessing and feature engineering through training and evaluation.
  • Ability to explain technical content and evaluation results clearly while collaborating with multiple stakeholders.
  • Ability to travel as needed for project or business requirements.
  • Business-level Japanese proficiency and conversational English proficiency.

Responsibilities

  • Design, implement, and advance machine-learning-based driver workload estimation algorithms using time-series data from vehicle CAN signals, in-vehicle sensors, and driver operation logs.
  • Build and operate training-data infrastructure and evaluation pipelines, including ground-truth data design, annotation policies, data preprocessing, and feature engineering.
  • Evaluate and improve logic for combining CAN-based workload indicators with other workload indicators, including surrounding-vehicle information and image-based indicators.
  • Design interfaces and specifications for integrating workload estimates into driving-suggestion and driver-alert functionality, and continuously improve the performance of the overall functionality.
  • Coordinate requirements and evaluation metrics with software engineers, test engineers, UX members, and other collaborators; organize and share experimental results and technical findings.
  • As project or business needs require, participate in planning and conducting simulator and real-vehicle evaluations, including travel, and drive validation and improvement cycles in real-world environments.

Desired Qualifications

  • Experience developing or evaluating automotive systems, advanced driver-assistance systems (ADAS), driver monitoring, robotics, or related systems.
  • Experience designing features or developing workload or driver-state estimation algorithms using vehicle CAN signals, in-vehicle sensors, driver operation logs, or other time-series data.
  • Experience deploying machine-learning models in production, including edge or embedded optimization, inference-pipeline development, or model operations.
  • Experience evaluating algorithms or generating data with simulators such as Unity.
  • External machine-learning or signal-processing contributions, such as competition participation or paper publication.
  • A bachelor's degree in computer science, electrical or electronic engineering, control engineering, or a related field, or equivalent professional experience.
  • Ability to work in Susono.

About the Company

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Woven

Develops global intelligent mobility solutions

Woven by Toyota develops and delivers advanced mobility technologies and services to Toyota customers worldwide, focusing on safer and more intelligent transportation. Its products and solutions are implemented in vehicles and related mobility services, combining software and hardware innovations with seamless user experiences to expand global access to mobility and enhance driver capabilities. What sets Woven apart is its blend of Silicon Valley innovation with traditional Japanese craftsmanship, enabling globally scalable mobility solutions that prioritize safety and reliability. The company’s goal is to create more personal, seamless, and capable mobility experiences for customers, while evolving with social, technological, and customer needs and advancing the future of intelligent transportation.

Company size
1,001-5,000
Company stage
Not listed
Total funding
$583.6M
Headquarters
Tokyo, Japan
Founded
2023

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