At Toyota Research Institute (TRI), we’re on a mission to improve the quality of human life. We’re developing new tools and capabilities to amplify the human experience. To lead this transformative shift in mobility, we’ve built a world-class team advancing the state of the art in AI, robotics, driving, and material sciences.
Our goal is to revolutionize the field of robotics, enabling long-horizon dexterous behaviors to be efficiently taught, learned, and improved over time in diverse, real-world environments with people.
Responsibilities
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Contribute to the design of novel robotic systems through software development, including control, perception, planning, with classical and learned methods; applying state-of-the-art methods, or creating new ones as needed.
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Enable research into learned models by working with hardware engineers, technicians, and researchers to build and integrate new robotics technologies.
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Work with robotics research scientists toward applying and integrating research toward more robust, perceptive, and scalable systems.
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Design and integrate creative system solutions; combining actuation, structure, and sensing, as well as new mechanisms and sensory for human-scale manipulation and dexterity.
Qualifications
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B.S. or higher in an engineering related field and 6+ years of relevant industry experience.
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Strong software engineering skills; very comfortable with working in mixed Python and C++ codebase.
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The ability to design and deploy integrated systems that complement and bring to bear advanced software and learning algorithms.
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Deep multi-functional understanding of all levels of a robotic system including both hardware and software, with experience operating robots.
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Experience with a full-stack approach to robotics, including familiarity with electromechanical systems and actuation, as well as practical software design.
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Familiarity with physical/embodied AI methods and training machine-learned policies.
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Experience with inter- and in-process communication, parallelism, logging, networking and data systems, and common methods.
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Familiarity with classical motion planning and robotic control.
Bonus Qualifications
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A track record of relevant publications in top international conferences (RSS, NeurIPS, ICML, CoRL, ICRA, IROS, …)
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Background or familiarity with some of the following: motion control and actuation, whole-body control, robot teleoperation methods, common communication protocols, research robotic arms/systems, visual perception and depth sensors, machine learning, robotic simulation, force and tactile sensing systems, haptic interfaces.