Senior AI Research Scientist, Robotics & Embodied AI

Samsung SDS America•Mountain View, CA

1d•$230,000 - $270,000•Onsite

About The Position

Samsung SDS America AI Science Lab is researching the next generation of intelligent robotic systems that perceive, reason, learn, and act in the physical world. Working with multiple robot hardware partners, we develop complete embodied AI systems spanning data collection through teleoperation, learning and policy development, simulation, and deployment on physical robots in real-world environments. We are looking for an AI Research Scientist, Robotics & Embodied AI to join our team and help advance the science behind intelligent robotic systems. You will research and develop learning-based approaches that enable robots to understand their environment, learn complex behaviors, manipulate objects, and perform tasks autonomously. This role sits at the intersection of artificial intelligence, machine learning, robotics, computer vision, and embodied intelligence. You will work on problems such as robot learning, imitation learning, reinforcement learning, vision-language-action models, multimodal perception, dexterous manipulation, and learning-based control. Unlike a traditional research role focused primarily on simulation or publications, you will have the opportunity to take research from hypothesis to experiment to physical robot deployment. You will work directly with humanoid and other robotic platforms, use teleoperation and real-world demonstrations to generate training data, develop and evaluate learning-based policies, and iterate through experiments on physical hardware. What sets our lab apart is the combination of research freedom and real-world impact. You will work alongside a small, highly technical team where researchers have direct ownership of their work—from defining research questions and developing novel approaches to implementing experiments and evaluating results on real robots. Samsung SDS builds and operates systems supporting manufacturing at scale across thousands of factory lines worldwide. The embodied AI capabilities we research today are being developed with this production environment in mind, with a long-term vision extending from individual intelligent robots to coordinated multi-robot systems operating in complex industrial environments. If you are excited about advancing AI from systems that understand the digital world to systems that can perceive, reason, learn, and act in the physical world, this opportunity to do research that moves directly into reality.

Requirements

Ph.D. in Computer Science, Robotics, Electrical Engineering, Mechanical Engineering, AI/ML, or a related technical field, or equivalent research experience
Strong research background in one or more areas of: Robotics, Embodied AI, Robot learning, Reinforcement learning, Imitation learning, Computer vision, Multimodal AI, Machine learning, Robot manipulation, Motion planning and control
Strong programming skills in Python and C++
Hands-on experience with modern machine learning frameworks, such as PyTorch
Experience designing and conducting research experiments with measurable results
Experience working with robotics simulation and/or physical robotic platforms
Strong understanding of machine learning fundamentals and experimental methodology
Ability to independently identify research problems, formulate hypotheses, implement solutions, and evaluate results

Nice To Haves

Research experience with humanoid robots or other advanced robotic platforms
Experience with Vision-Language-Action (VLA) models, multimodal foundation models, or generative AI for robotics
Experience with dexterous manipulation, grasping, locomotion, or contact-rich robotics
Experience with reinforcement learning and/or imitation learning for real-world robotics
Experience with Isaac Sim, MuJoCo, SAPIEN, PyBullet, or similar simulation environments
Experience with ROS/ROS2
Experience collecting and processing large-scale robot demonstration datasets
Experience with teleoperation systems and human demonstration data
Experience with sim-to-real transfer and domain randomization
Experience optimizing AI inference and robotics systems for real-time deployment
Publications in leading AI/robotics conferences or journals such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, CoRL, RSS, ICRA, or IROS
Contributions to open-source robotics or AI projects
Experience taking research prototypes from experimentation to robust physical systems
Experience working with NVIDIA GPU platforms and CUDA-based AI/robotics workloads

Responsibilities

Conduct original research in robotics, embodied AI, physical AI, and machine learning
Develop novel algorithms and learning approaches for intelligent robotic systems
Research and develop methods for robot learning, imitation learning, reinforcement learning, and learning-based control
Explore vision-language-action and multimodal models for robotic perception, reasoning, and action
Develop approaches that enable robots to learn complex manipulation, grasping, locomotion, and interaction behaviors
Design experiments using real-world robot platforms, simulation environments, and large-scale robotics datasets
Develop and evaluate data collection pipelines using teleoperation, demonstrations, and other forms of human-guided robot interaction
Train, evaluate, and deploy learning-based policies on physical robotic platforms
Research dexterous manipulation, grasping, and contact-rich interaction for humanoid and other robotic systems
Investigate methods for transferring learned behaviors from simulation and synthetic data to real-world robots
Develop scalable training and evaluation pipelines for embodied AI models
Integrate research algorithms with robot perception, sensors, actuators, and low-latency control systems
Analyze experimental results, identify limitations, and formulate new research directions
Collaborate with robotics engineers, AI researchers, and hardware teams to translate research concepts into working robotic systems
Contribute to research publications, technical reports, patents, and intellectual property where appropriate
Stay current with advances in embodied AI, foundation models, robot learning, computer vision, reinforcement learning, and related research areas
Contribute to the architecture and long-term research direction of next-generation embodied AI systems