Robotics Applied Scientist

Job description

We are seeking an Applied Scientist to develop tactile- and force-driven manipulation policies for highly dexterous, multi-fingered robotic hands and grippers. In this role you will research and build policy learning algorithms that enable robotic hands to perform grasping and in-hand manipulation in unstructured, real-world environments, using touch and force feedback as first-class signals rather than afterthoughts. Much of this work is grounded in reinforcement learning.

To be successful you need to be highly motivated, dive deep, and deliver to the highest standards. You will demonstrate strong working knowledge of modern policy learning methods, with expertise in taking algorithms from…

Job requirements

Experience programming in Java, C++, Python or related language
Research or applied experience in one or more of: tactile sensing, manipulation policy learning, contact-rich manipulation, dexterous grasping, or multi-modal perception for robotics
Experience deploying learned policies or control algorithms on physical robotic hardware
Experience leading technical initiatives and key deliverables
Experience bridging research with practical engineering implementation in physical robotic systems
Hands-on experience with modern tactile sensing hardware and tactile signal processing
Strong publication record at major robotics/ML venues (e.g., RSS, CoRL, ICRA, IROS, NeurIPS, ICML, ICLR), including impactful first-author work in tactile sensing or dexterous manipulation
Experience developing manipulation policies using reinforcement learning, imitation learning, or foundation models for contact-rich tasks

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