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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Research Scientist / Engineer - Robot Learning - **Company:** Rhoda ai - **Location:** Palo Alto, CA, United States - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Middleware, Reinforcement Learning, Pytorch - **Published:** May 19, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=27021f39f1bb5116 ## About the Role * 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 ## Description * 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 ## Related Videos - [On the straight and narrow path - How to get cars to drive themselves using reinforcement learning and trajectory optimization](https://www.wearedevelopers.com/videos/205-on-the-straight-and-narrow-path-how-to-get-cars-to-drive-themselves-using-reinforcement-learning-and-trajectory-optimization) - [Robots are coming into the wild! Full-Stack Robotics Engineers, be ready!](https://www.wearedevelopers.com/videos/479-robots-are-coming-into-the-wild-full-stack-robotics-engineers-be-ready) - [Developer’s Perspective: Overview of the Tezos Blockchain Ecosystem](https://www.wearedevelopers.com/videos/237-developer-s-perspective-overview-of-the-tezos-blockchain-ecosystem) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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