At Rhoda AI, we're building the full-stack foundation for the next generation of humanoid robots — from high-performance, software-defined hardware to the foundational models and video world models that control it. Our robots are designed to be generalists capable of operating in complex, real-world environments and handling scenarios unseen in training. We work at the intersection of large-scale learning, robotics, and systems, with a research team that includes researchers from Stanford, Berkeley, Harvard, and beyond. We're not building a feature; we're building a new computing platform for physical work — and with over $400M raised, we're investing aggressively in the R&D, hardware development, and manufacturing scale-up to make that a reality.

We're looking for a Research Scientist or Research Engineer to advance dexterous manipulation — enabling our robots to perform contact-rich, fine-motor tasks that require precision, physical reasoning, and adaptability to novel objects and environments.

What You'll Do

• Research and develop learning-based approaches for dexterous and contact-rich manipulation tasks

• Design training strategies and data collection protocols for fine-motor and multi-finger manipulation

• Work on perception for manipulation: contact detection, tactile sensing, object pose estimation, and spatial reasoning

• Build and evaluate policies that generalize to novel objects and unstructured environments

• Develop simulation environments and benchmarks for dexterous manipulation research

• Collaborate with robot hardware, perception, and learning teams to close the sim-to-real gap

• Publish and present work at top-tier robotics and ML venues (especially valued for RS track)

What We're Looking For

• Strong background in robot learning, manipulation, or physical AI

• Hands-on experience developing and evaluating manipulation policies on real hardware

• Understanding of contact mechanics, grasp planning, or tactile sensing

• Solid ML skills with experience in imitation learning, RL, or diffusion-based policies

• Ability to work across the stack from simulation to real robot deployment

Nice to Have (But Not Required)

• PhD in Robotics, ML, or a related field

• Publication record at ICRA, CoRL, RSS, NeurIPS, or related venues

• Prior work on dexterous hands, multi-finger manipulation, or contact-rich tasks

• Experience with tactile sensors or force/torque feedback in robot learning

• Familiarity with simulation tools for manipulation (MuJoCo, Isaac Sim, Genesis)

• Experience with skill libraries, language-conditioned manipulation, or task parameterization

Why This Role

• Push the frontier on one of the hardest open problems in robotics

• Work with hardware and data resources that few research labs have access to

• Direct path from research results to deployment on our humanoid platform

• Tight collaboration across robot learning, hardware, and systems teams