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 Engineers to work closely with this team on end-to-end model development. This is a hands-on role spanning the full stack: data, infrastructure, model training, and deployment. You'll help turn research ideas into scalable, working systems — including learning and leveraging world models for planning, prediction, and control.

What You'll Do

• Design and implement foundational models and world models for large-scale robotic learning

• Build and maintain data pipelines (collection, curation, filtering, augmentation) for multimodal robotic data (vision, proprioception, actions, language, video)

• Work on pre-training and post-training (fine-tuning, alignment, evaluation) of large models and world models

• Implement and experiment with different model architectures

• Develop training and evaluation frameworks for world models, including rollout quality, long-horizon prediction, and downstream task performance

• Optimize training infrastructure and workflows (distributed training, efficiency, debugging)

• Collaborate closely with researchers to translate ideas into robust, scalable implementations

• Support experiments, ablations, and real-world deployment on robotic systems

What We're Looking For

• Strong software engineering skills with a research mindset

• Experience implementing ML models end-to-end, not just running existing code

• Familiarity with the full ML pipeline: data → pre-training → post-training → evaluation → deployment

• Solid foundation in deep learning and modern ML frameworks (e.g., PyTorch, JAX)

• Ability to reason about and debug complex learning systems, including world model training and usage

• Comfortable working in an ambiguous, fast-moving startup environment

Nice to Have (But Not Required)

• Publications at top ML/robotics conferences (e.g., NeurIPS, ICML, ICLR, CoRL, RSS, ICRA)

• PhD/Masters or equivalent research experience

• Experience with world models or generative models for control

• Experience working with large models (LLMs, vision-language models, video models, large-scale policy models)

• Experience with large-scale training infrastructure (distributed training, clusters, cloud or on-prem systems)

Why This Role

• Work with an elite research team from Stanford, Berkeley, Harvard, and beyond

• Work on foundational models and world models for real-world robotics — not toy environments

• Tight collaboration between research and engineering (no silos)

• Direct connection between research ideas and real robotic behavior

• High ownership and impact in a small, ambitious team