Research Scientist / Engineer - Robot Learning

• 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, * Your work is what makes our robots actually perform tasks reliably in the real world - the direct connection between pre-trained capability and deployed behavior
• Work at a rare intersection: state-of-the-art video generation models applied to real robot hardware, not simulation
• Fast feedback loop between model changes and real robot performance
• High ownership on a small team where robotics domain expertise is core to the mission

• 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 models and how they connect to action prediction
• PhD in Robotics, ML, or a related field
• Publication record at ICRA, CoRL, RSS, NeurIPS, or related venues

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.