Research Scientist / Engineer - Reasoning

Overview 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 possibly 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. What You'll Do Research and develop methods for multi-step reasoning and planning grounded in embodied world models Design architectures and training strategies that improve compositional generalization and long-horizon prediction Explore chain-of-thought reasoning, process reward models, and test-time search in the context of robotic control Build evaluation benchmarks for reasoning and planning capabilities applied to physical tasks Investigate how world model rollouts can enable planning and decision-making at inference time Collaborate with pre-training and post-training teams to integrate reasoning capabilities into the full model pipeline Publish and present work at top-tier venues (especially valued for RS track) What We\'re Looking For Strong background in reasoning, planning, or search with large models Deep understanding of sequence modeling, transformer architectures, and generative models Experience with test-time compute methods (beam search, MCTS, self-consistency, verifiers, etc.) Strong research taste and ability to identify high-leverage directions Fluency with PyTorch or JAX and ability to implement and iterate on research ideas end-to-end Staff-level candidates are expected to define technical direction and drive research strategy independently; senior/MTS candidates execute complex projects with strong fundamentals and growing scope Nice to Have (But Not Required) PhD in ML, Robotics, or a closely related field Publication record at NeurIPS, ICML, ICLR, CoRL, or related venues Prior work on reasoning in LLMs (chain-of-thought, process reward models, search-based methods) Experience with model-based planning or hierarchical reinforcement learning Familiarity with long-horizon prediction, video generation, or world model rollouts Experience with embodied AI or robotic planning problems Why This Role Tackle one of the hardest open problems in embodied AI: enabling robots to reason about what to do next Research that directly translates to robot behavior in complex, real-world scenarios High research freedom grounded in real task performance Tight collaboration with pre-training, post-training, and robotics teams #J-18808-Ljbffr Rhoda ai