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.

What You'll Do

• Drive research on foundational models and world models for robotics (representation learning, dynamics/prediction, planning, control)

• Formulate research problems and hypotheses grounded in real robotic autonomy needs

• Design and run rigorous experiments at scale, including ablations, benchmarking, and evaluation methodology

• Develop and evaluate model architectures for long-horizon prediction, rollout quality, and downstream robotic task performance

• Explore and advance pre-training and post-training (fine-tuning, alignment, evaluation) of large multimodal models

• Collaborate closely with Research Engineers to translate new ideas into scalable training pipelines and reliable systems

• Communicate results clearly through internal writeups, talks, and research reviews

• Publish and present work at top-tier venues

What We're Looking For (Required)

• PhD in a relevant field (e.g., ML, Robotics, Computer Science, Electrical Engineering, Applied Math, Computer Vision, or closely related)

• Strong publication record demonstrating high-quality research output (e.g., NeurIPS, ICML, ICLR, CoRL, RSS, ICRA, CVPR, etc.)

• Deep understanding of modern machine learning, with relevance to at least several of:

  • Deep learning and representation learning

  • Sequence modeling / transformers

  • Generative modeling (e.g., diffusion, autoregressive, latent-variable models)

  • Model-based learning, planning, and/or control

  • RL / imitation learning for robotics

• Strong research taste and independence: ability to define problems, execute, interpret results, and iterate quickly

• Proficiency with at least one modern ML stack (e.g., PyTorch or JAX) and the ability to implement research ideas in code

• Clear written and verbal communication skills

• Comfort operating in ambiguity in a fast-moving startup environment

Nice to Have (But Not Required)

• Prior work specifically on world models (latent dynamics, predictive models, model-based RL/planning, long-horizon rollouts)

• Experience with large-scale multimodal training (VLMs, video models, action-conditioned models, large policy models)

• Experience working with robotic learning data (real-world logs, teleop, simulation-to-real, multimodal sensor streams)

• Hands-on experience deploying learning-based components on real robots

• Familiarity with distributed training and performance debugging (multi-GPU / multi-node)

Why This Role

• Work with an elite research team from Stanford, Berkeley, Harvard, etc.

• Research that directly connects to real-world robotic autonomy — not toy benchmarks

• Tight collaboration between research and engineering (no silos)

• High ownership and ability to shape the research agenda

• Opportunity to publish meaningful work while seeing it come alive on real robotic systems