(Senior) AI Engineer - Reinforcement Learning Manipulation

ppRIVR, part of Amazon is a robotics company pioneering Physical AI through real-world doorstep delivery. Founded in 2024 as an ETH Zurich spin-off, RIVR developed wheeled-legged robots designed to operate in complex, unstructured environments such as stairs, gates, doors, and uneven urban terrain. We believe that achieving general physical intelligence requires solving real customer problems in the real world, where robots can learn from rich operational data at scale. /p pFollowing our acquisition by Amazon in March 2026, we are continuing this mission with greater reach and speed. By combining custom robot hardware, onboard autonomy, and cloud-based coordination, RIVR, part of Amazon is building the next generation of safe, reliable autonomous robots for last-mile delivery /p h3bJob description: Dexterous Manipulation RL /b /h3 pReinforcement learning is transforming our robotic intelligence, enabling autonomous behavior without human guidance. We are seeking a Senior AI Engineer with deep expertise in reinforcement learning and deep learning, including supervised and self-supervised learning with a focus on dexterous manipulation. Your role will involve leveraging both simulated and real-world data to address practical challenges in dynamic grasping, contact-rich manipulation, and object interaction. If you are passionate about advancing AI and developing innovative solutions, join us in shaping the future of intelligent robotics. /p h3What you’ll be doing /h3 ul liDevelop cutting-edge reinforcement learning algorithms to enable robust, contact-rich dexterous manipulation, translating vision, depth, tactile, and proprioceptive sensor input into precise end-effector and joint-level motor commands. /li liDesign, test, and refine algorithms to solve complex real-world manipulation challenges, such as handling diverse package form factors, dynamic hand-offs, and operating door handles or latches. /li liCollaborate with the foundation model team to innovate methods that leverage both simulated and real-world data. /li /ul h3What you must have /h3 ul liStrong background in brobotic manipulation, including dynamics, grasp synthesis, and trajectory optimization. /b /li liMaster’s degree or higher in a relevant field such as Engineering, Robotics, or Machine Learning. /li liA minimum of five years of industry or research experience, with PhD experience applicable. /li liStrong deep learning fundamentals, including supervised and self-supervised learning techniques, and reinforcement learning, including Markov Decision Processes (MDPs), neural network architectures, policy optimization algorithms, model-based vs. model-free RL, exploration-exploitation strategies, value function methods, transfer learning, domain adaptation, sim-to-real transfer, etc. /li liStrong background in robotics including autonomy and/or manipulation. /li liExperience with deploying artificial neural networks on hardware platforms. /li liAbility to write production-level code in modern C++. /li liAbility to prototype algorithms and train deep neural networks in Python. /li /ul h3Get some bonus points /h3 ul liPhD degree in Robotics, Engineering, Computer Science, Machine Learning or a similar discipline, or an equivalent amount of research experience. /li liPublications at top-tier conferences (e.g., ICRA, IROS, CoRL, RSS) specifically focusing on robotic manipulation, grasping, or contact-rich RL. /li liDemonstrated experience working with tactile sensing, multi-fingered robotic hands, or bimanual manipulation. /li /ul pRIVR, part of Amazon is committed to building a diverse and inclusive team that values every perspective. If you’re passionate about driving innovation in robotics and creating meaningful impact, and bring your unique self to our team. /p pWe believe the best work is done when collaborating and therefore require in-person presence in our office locations. /p /p #J-18808-Ljbffr