Machine Learning Engineer, Driverless Vehicles

Woven Planet

Woven Planet Group (Woven Planet) represents a carefully curated blend of expertise and resources dedicated to bringing the vision of “Mobility to Love, Safety to Live” to life. Through innovations and investments in automated driving, robotics, smart cities, and more, we are transforming how humankind lives, works, and moves. We exist to design, build, and deliver secure, connected, and sustainable mobility solutions that benefit all people worldwide. Founded in 2018 as Toyota Research Institute – Advanced Development (TRI-AD), Woven Planet is composed of four complementary companies: Woven Planet Holdings, Woven Core, Woven Alpha, and Woven Capital.

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Aimed at enabling mobility for all, our team’s mission is to develop a fully autonomous vehicle capable of driving with no human involvement. We focus on Mobility as a Service (MaaS) applications, as increased vehicle use, common in rideshare, allows us to use rich sensors and computers to achieve the vital reliability for removing a human driver. We plan to explore a variety of approaches, including telemonitoring and remote assistance services to help us achieve our goals. We strive to make a significant and positive impact on people’s lives with our fully driverless vehicles. While working towards our mission we strive to build a diverse team of curious and dedicated individuals with a wide range of experiences. Teamed up with partners around the world as a global community, we strive to make a significant and positive impact on people’s lives with our fully driverless vehicles.

As a Machine Learning Engineer, you will work alongside the engineers in our team and Machine Learning Engineers in other teams. You will be responsible for maintaining and improving the ML infrastructures for autonomous driving technology. We are looking for engineers who are passionate about building tools and ML infrastructures to enable productive ML models development in autonomous driving technology.




  • Build workflows to build ML applications for autonomous vehicles
  • Improve and develop ML pipeline (training pipeline, annotation pipeline, and data ingestion pipeline) and other tools, to increase the efficiency of perception engineers to develop machine learning models
  • Design, communicate, and build new features to meet the needs in our team
  • Collaborate with other Machine Learning Engineers in other teams to develop ML pipeline and tools



  • Bachelor’s degree in machine learning, computer science, or a related field
  • 3+ years of experience in Machine Learning Area and understanding of machine learning development life cycle
  • Strong Python skills with strong ability to write high quality, unit-testable code
  • Experience in constructing a system using one of the cloud service AWS, Azure, GCP
  • Comfortable and proficient to work in English



  • Master’s in machine learning, computer science, or a related field
  • Experience to build ML pipeline
  • Experience in engaged in an automated driving project
  • Proficiency in writing C++
  • Experience in working in an agile environment with modern software development tools such as JIRA, Confluence, etc.


    If you are currently located outside of Japan, don’t worry, we’ll set an interview over Google Hangout Meet or Skype.
    ・Competitive Salary – Based on skills and experience
    ・Work Hours – Flexible working time with NO core-hours
    ・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 the company
    ・Japanese Social Security – all applicable (Health Insurance, Pension, Workers’ Comp, and Unemployment Insurance, Long-term care insurance)
    ・In-house Training Program (software study/language study)
    By submitting your application you agree to the following terms:
    ・We are an equal opportunity employer and value diversity.
    ・We pledge that any information we receive from candidates will be used ONLY for the purpose of hiring assessment.