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 possible by our cutting edge research and end-to-end system design. We've raised over $450M and are investing aggressively in model research, infrastructure, hardware development, and manufacturing scale-up to make generalist robotics a reality.

We're looking for Research Scientists and Research Engineers with deep robotics or autonomous systems domain knowledge to adapt our web-pretrained video model to real robot tasks. Post-training at Rhoda means taking a causal video generation model pretrained on internet-scale data and fine-tuning it on robot-collected demonstrations to produce reliable, generalizable behavior — with as little task-specific data as possible. We hire across levels — from senior to staff.

What You'll Do

• Design and implement RL training pipelines to improve robot policy performance beyond what imitation learning alone achieves — reward design, online data collection, and policy optimization
• Develop and apply RL algorithms (PPO, GRPO, or similar) adapted to the video prediction setting, including reward modeling and feedback collection strategies for physical task performance
• Design and implement broader post-training pipelines: supervised fine-tuning, preference optimization, and behavioral alignment on robot-collected demonstration data
• Work on the inverse dynamics model that translates video predictions into executable robot actions
• Build evaluation frameworks for post-trained policies: task success, generalization to novel objects and environments, and failure mode analysis on real hardware
• Research methods to efficiently adapt models to new tasks with minimal demonstration data, including in-context generalization and few-shot adaptation
• Identify failure modes and systematic weaknesses in deployed robot policies and drive targeted improvements
• Iterate quickly between simulation and real robot evaluation to close the feedback loop
• Collaborate with the pre-training team to surface what capabilities are missing from the base model and need to be addressed upstream

What We're Looking For