Robotics Intern
Mobile Manipulation, Behaviors
Updated on 11/6/2023
Toyota Research Institute

201-500 employees

R&D arm of Toyota
Company Overview
Toyota Research Institute's mission is to improve the quality of human life through advances in artificial intelligence, automated driving, robotics, and materials science. They're dedicated to building a world of “mobility for all” where everyone, regardless of age or ability, can live in harmony with technology to enjoy a better life.
AI & Machine Learning
Automotive & Transportation
Robotics & Automation

Company Stage


Total Funding





Los Altos, California

Growth & Insights

6 month growth


1 year growth


2 year growth

Mountain View, CA, USA
Experience Level
Desired Skills
Data Structures & Algorithms
AI & Machine Learning
Mechanical Engineering
  • B.S. or M.S. in an engineering related field
  • Experience with inventing and deploying innovative autonomous behaviors for robotic systems in real-world environments
  • Experience in areas such as reactive control, trajectory optimization, coordinated whole-body control, dexterous manipulation, arm motion planning, grasp planning, navigation, and human interaction
  • Experience in applying machine learning to robotics, including areas such as reinforcement, imitation, and transfer learning
  • Strong software engineering skills, preferably in C++, and analysis and debugging of autonomous robotic systems
  • A team player with strong communication skills and a willingness to learn from others
  • Passionate about seeing robotics have a real-world, large-scale impact
  • Develop, integrate, and deploy algorithms linking perception to autonomous robot actions, including manipulation, navigation, and human-robot interaction
  • Invent and deploy innovative solutions at the intersection of machine learning, mobility, manipulation, human interaction, and simulation for performing useful, human-level tasks in human environments
  • Invent novel ways to engineer and learn robust, real-world behaviors, including using optimization, planning, reactive control, self-supervision, active learning, learning from demonstration, simulation and transfer learning, and real-world adaptation