Research Scientist / Engineer - Post-training & Robot Learning

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 $400M 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 - Hands-on experience with robot systems, robotic policy learning, or autonomous systems in an industry or research setting (robotics, self-driving, or similar physical AI domains) - Strong understanding of robot policy learning: imitation learning, behavior cloning, and how RL builds on top of it - Practical familiarity with real robot hardware, deployment constraints, and sensor modalities (vision, proprioception) - Solid ML skills with hands-on PyTorch experience - Ability to diagnose policy failures, reason about distribution shift, and iterate effectively on data and training strategies - Comfort with ambiguity and fast-changing research priorities - Staff-level candidates are expected to define technical direction and drive research strategy independently; senior candidates execute complex projects with strong fundamentals and growing scope Nice to Have (But Not Required) - Hands-on experience with reinforcement learning — reward design, policy optimization, and online RL training loops — applied to real or near-real environments (robotics, games, simulated physics, or similar); this is a significant plus - Prior industry experience in robotics, autonomous driving, or physical AI (e.g., manipulation, mobile robotics, self-driving stacks) - Experience with teleoperation systems or robot demonstration collection at scale - Familiarity with robot middleware (ROS/ROS2) and real-time control systems - Experience with simulation environments for robotics (MuJoCo, Isaac Sim, Genesis) - Understanding of video generation m