Research Member of Technical Staff- Reasoning

Research Member of Technical Staff- Reasoning LocationMountain ViewEmployment TypeFull timeDepartmentResearchCompensation$200K – $300K • Offers EquityAt 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 Research Scientists and Research Engineers to advance the reasoning and planning capabilities of our foundation world models — enabling robots to decompose goals, plan multi-step actions, and handle long-horizon tasks in complex, unstructured environments. We hire across levels — from senior to staff.What You'll DoResearch and develop methods for multi-step reasoning and planning grounded in embodied world modelsDesign architectures and training strategies that improve compositional generalization and long-horizon predictionExplore chain-of-thought reasoning, process reward models, and test-time search in the context of robotic controlBuild evaluation benchmarks for reasoning and planning capabilities applied to physical tasksInvestigate how world model rollouts can enable planning and decision-making at inference timeCollaborate with pre-training and post-training teams to integrate reasoning capabilities into the full model pipelinePublish and present work at top-tier venues (especially valued for RS track)What We're Looking ForStrong background in reasoning, planning, or search with large modelsDeep understanding of sequence modeling, transformer architectures, and generative modelsExperience with test-time compute methods (beam search, MCTS, self-consistency, verifiers, etc.)Strong research taste and ability to identify high-leverage directionsFluency with PyTorch or JAX and ability to implement and iterate on research ideas end-to-endStaff-level candidates are expected to define technical direction and drive research strategy independently; senior/MTS candidates execute complex projects with strong fundamentals and growing scopeNice to Have (But Not Required)PhD in ML, Robotics, or a closely related fieldPublication record at NeurIPS, ICML, ICLR, CoRL, or related venuesPrior work on reasoning in LLMs (chain-of-thought, process reward models, search-based methods)Experience with model-based planning or hierarchical reinforcement learningFamiliarity with long-horizon prediction, video generation, or world model rolloutsExperience with embodied AI or robotic planning problemsWhy This RoleTackle one of the hardest open problems in embodied AI: enabling robots to reason about what to do nextResearch that directly translates to robot behavior in complex, real-world scenariosHigh research freedom grounded in real task performanceTight collaboration with pre-training, post-training, and robotics teamsApply for this Job