Research Scientist / Engineer - Reasoning

Research Scientist / Engineer - Reasoning 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 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 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