Senior Robotics Applied Scientist in Sunnyvale, California, United States, to develop tactile- and force-driven manipulation policies for dexterous robotic hands, focusing on sim-to-real transfer and real-world manipulation. You will research and build policy learning algorithms enabling grasping and in-hand manipulation in unstructured environments, using touch and force feedback as primary signals. The role emphasizes translating research into deployable robotic capabilities, collaborating with hardware, mechanical design, firmware, and controls teams, and mentoring engineers and interns. Responsibilities include designing and training manipulation policies with reinforcement learning, imitation learning, or hybrid approaches; driving sim-to-real transfer; shaping objectives with hardware constraints; building evaluation frameworks; publishing at top robotics and ML venues; and contributing to the team’s scientific rigor through code and design reviews. A strong candidate will have a PhD or a Master’s with 6+ years of applied research, proficiency in Java/C++/Python, and experience in tactile sensing, manipulation policy learning, contact-rich manipulation, and dexterous grasping. Preferred qualifications include leading technical initiatives, bridging research with practical engineering in physical robotic systems, hands-on tactile sensing experience, and a track record of publications in major venues. This is an equal opportunity employer.

Senior Robotics Applied Scientist in Sunnyvale, California, United States, to develop tactile- and force-driven manipulation policies for dexterous robotic hands, focusing on sim-to-real transfer and real-world manipulation. You will research and build policy learning algorithms enabling grasping and in-hand manipulation in unstructured environments, using touch and force feedback as primary signals. The role emphasizes translating research into deployable robotic capabilities, collaborating with hardware, mechanical design, firmware, and controls teams, and mentoring engineers and interns. Responsibilities include designing and training manipulation policies with reinforcement learning, imitation learning, or hybrid approaches; driving sim-to-real transfer; shaping objectives with hardware constraints; building evaluation frameworks; publishing at top robotics and ML venues; and contributing to the team’s scientific rigor through code and design reviews. A strong candidate will have a PhD or a Master’s with 6+ years of applied research, proficiency in Java/C++/Python, and experience in tactile sensing, manipulation policy learning, contact-rich manipulation, and dexterous grasping. Preferred qualifications include leading technical initiatives, bridging research with practical engineering in physical robotic systems, hands-on tactile sensing experience, and a track record of publications in major venues. This is an equal opportunity employer.