Research Scientist​/Engineer - Dexterous Manipulation

At Rhoda AI, we’re building the next generation of generalist intelligent robots. We own the full robotics stack from high-performance hardware and robot systems to the infrastructure and state‑of‑the‑art foundation world models that control our robots. Our robots are designed to be generalists capable of operating in complex, real‑world environments and handling long‑tail edge cases, made possible by our cutting edge research and end‑to‑end system design.

We've raised over $400M and are investing aggressively in model research, infrastructure, hardware development, and manufacturing scale‑up to make generalist robotics 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.

• 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)

• 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

• 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 (Mu Jo Co , Isaac Sim, Genesis)
• Experience with skill libraries, language‑conditioned manipulation, or task parameterization

• 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