Job Description
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