Research Engineer/Research Scientist - Dexterous Manipulation
AI Summary
Research Scientist or Research Engineer role focused on advancing dexterous manipulation for humanoid robots, including learning-based methods, perception, simulation, and real-world deployment.
About this role
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