Research Scientist / Engineer - Dexterous Manipulation

Rhoda Ai

Palo Alto, US

Who this role is best for

Geared toward mid-level researchers comfortable with real-world robot deployment who push the frontier on dexterous manipulation.

Best fit for

Researchers with hands-on robot learning experience and hardware deployment skills.
— “Hands-on experience developing and evaluating manipulation policies on real hardware”
Candidates who bridge simulation and real-world robotics effectively.
— “Ability to work across the stack from simulation to real robot deployment”
Engineers focused on contact-rich manipulation and tactile sensing applications.
— “Understanding of contact mechanics, grasp planning, or tactile sensing”

Things to consider

Expect tight collaboration across multiple technical teams beyond individual research.
— “Collaborate with robot hardware, perception, and learning teams”
Role involves publishing at top venues for research track candidates.
— “Publish and present work at top-tier robotics and ML venues”

How to stand out

Highlight specific examples of sim-to-real transfer in past projects.
— “close the sim-to-real gap”
Detail any tactile sensor integration or force feedback implementations.
— “Experience with tactile sensors or force/torque feedback in robot learning”
Showcase publications or prototypes involving multi-finger manipulation tasks.
— “Prior work on dexterous hands, multi-finger manipulation, or contact-rich tasks”

Pace · SteadyCollaboration · HighAutonomy · HighDecision Impact · CompanyLevel · Senior

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

researched and developed learning-based approaches
designed training strategies
built and evaluated policies

Typical background

roboticsAI research

Skills & requirements

Required

RoboticsDexterous ManipulationContact-rich TasksFine-motor TasksPhysical Reasoning

Preferred

Tactile SensingObject Pose EstimationSpatial ReasoningSkill Libraries

Stack & domain

Robot LearningManipulationPhysical AiImitation LearningReinforcement LearningDiffusion-based PoliciesContact MechanicsGrasp PlanningTactile SensingObject Pose EstimationSpatial ReasoningRoboticsMachine Learning

About the role

Original posting from Rhoda Ai via Ashby

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.
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

Source: Rhoda Ai careers (Ashby)