About Us2019-02-22T10:19:58+08:00

World Class Engineering Team

GT‘s core team came from industry leading Japanese and American AI technology companies all with more than 15 years of working experience in AI fundamental research and computer vision application implementation. The team’s AI experts had worked and studied in world class institutions such as Sony, Hewllet-Packard, NTT Data, Pioneer, Softbank, EMC, UC Berkeley, ZheJiang University, TongJi University

Global Vision

We have rich international background with a global vision – Our headquarter is located in ShangHai and our R&D center is based in WuXi. We have joint labs with technology enterprises in Tokyo and Silicon Valley. We have also established research partnership with ZheJiang University, China University of Mining and Technology and UC Berkeley to accelerate AI technology advancement and application development for traditional industries

Join Us

Join GT, you will be among world-class engineers, experts and professors working together to change the world

Main responsibility:

For this position we’re looking to hire full time a great Computer Vision Engineer to help us develop and train algorithms to analyze relevant data provided by our enterprise customer. You will be working together with the rest of the engineering and product team, building products while clearly documenting your work.

Required Skills:

• Working experience developing product-level computer vision algorithms
• Strong proficiency in Machine Learning, especially deep learning
• Experience with C++ and Python and working knowledge of Linux/Unix
• Familiarity with OpenCV, Matlab or ROS tools
• English communication and technical document research skills

Main responsibility:

  • Multi-sensor information processing algorithm development

Required Skills:

  • Working knowledge of signal analysis and data integration
  • Experience with C++ and Python and working knowledge of Linux/Unix
  • Familiarity with OpenCV, Matlab or ROS tools
  • English communication and technical document research skills