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 $450M 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.
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