About the role
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
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.
Enable research into learned models by working with hardware engineers, technicians, and researchers to build and integrate new robotics technologies.
Work with robotics research scientists toward applying and integrating research toward more robust, perceptive, and scalable systems.
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
B.S. or higher in an engineering related field and 6+ years of relevant industry experience.
Strong software engineering skills; very comfortable with working in mixed Python and C++ codebase.
The ability to design and deploy integrated systems that complement and bring to bear advanced software and learning algorithms.
Deep multi-functional understanding of all levels of a robotic system including both hardware and software, with experience operating robots.
Experience with a full-stack approach to robotics, including familiarity with electromechanical systems and actuation, as well as practical software design.
Familiarity with physical/embodied AI methods and training machine-learned policies.
Experience with inter- and in-process communication, parallelism, logging, networking and data systems, and common methods.
Familiarity with classical motion planning and robotic control.
Bonus Qualifications
A track record of relevant publications in top international conferences (RSS, NeurIPS, ICML, CoRL, ICRA, IROS, …)
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.
Tech stack
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