Researcher

Lab Summary: The Robot Intelligence Lab at Samsung Research America is a new facility dedicated to advancing the field of robotics through cutting-edge research and development. The lab’s mission is to develop advanced technologies to power intelligent robotic systems capable of manipulation, navigation, and complex reasoning for a variety of high-impact application areas. To achieve this mission, the lab collaborates closely with external entities such as universities, startups, and government labs, as well as with several Samsung Product and Advanced Research labs.

Position Summary:  As the Robot Intelligence Lab grows, we will be hiring across multiple career levels including Senior, Staff and Senior Staff. The level at which a candidate is hired will reflect their experience, technical expertise and demonstrated impact.

Regardless of level we are looking to grow the team with Researchers who have solid technical skills and rich academic and/or industry experience in areas of robotics and embodied intelligence. Our ideal candidate explores novel technologies aimed towards generalizing robots to perform tasks in the real world. Samsung’s unique advantage in the consumer electronics market and growing focus on AI and robotics will provide you with exciting technical challenges and a rewarding career experience. By leveraging Samsung’s vast product ecosystem to deliver novel user experiences, your work will define Samsung’s future in robotics and significantly impact real-world users.

We are hiring candidates for multiple groups within the lab (Reasoning, Dexterity, Data Efficiency, and Robot Systems), and hence the responsibilities and required skills for each position will be group dependent.

Position Responsibilities:

• Conduct state-of-the-art research to push boundaries of emerging fields of robotics, on topics such as task-and-motion planning (TAMP), world action models (WAMs), vision-language-action (VLA) models, vision language models (VLMs), open-world 3D perception, dexterous manipulation, real2sim2real, whole-body control, and multimodal (vision, tactile, audio, semantic) data fusion
• Work with team to explore novel techniques, device interfaces, and model architectures to train robotic policies effectively with limited available data
• Design autonomy architectures that interpret high-level task instructions and generate low-level task commands
• Enable real-time video encoders with multi-view inputs for robust scene understanding, enable VLMs to reason about general task knowledge, including tool usage, object interactions, and task feasibility assessment, and enable Chain-of-Thought (CoT) task reasoning on VLM for complex, multi-step task execution
• Develop efficient 3D scene graph representation and other 3D semantic/geometric models/solutions
• Scale robot evaluation pipelines across robot fleets with a combination of simulation and real-world testing