(Senior) AI Engineer - Reinforcement Learning Manipulation
RIVRJobsCloseBy Editorial Insights
RIVR, part of Amazon, is seeking a Senior AI Engineer specializing in reinforcement learning for dexterous manipulation to push autonomous doorstep delivery forward with advanced robotic hands. The role blends simulated and real world data to solve dynamic grasping, contact rich manipulation and object interaction, and involves close collaboration with the foundation model team. You must have a strong background in robotic manipulation (dynamics, grasp synthesis, trajectory optimization), a Master’s degree or higher, and at least five years of relevant experience, with deep learning and RL fundamentals including sim to real, plus the ability to write production level C++ and prototype in Python. Bonus points for a PhD, top tier publications, tactile sensing or multi fingered hands. Tips for applying: tailor your CV to emphasize real world deployments, quantify outcomes, showcase a portfolio of code and demos, and confirm in person availability in Zurich as the role requires onsite presence.
RIVR, 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.
Following 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
Job description: Dexterous Manipulation RL
Reinforcement 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.What you’ll be doing
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Develop 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.
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Design, 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.
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Collaborate with the foundation model team to innovate methods that leverage both simulated and real-world data.
What you must have
- Strong background in robotic manipulation, including dynamics, grasp synthesis, and trajectory optimization.
- Master’s degree or higher in a relevant field such as Engineering, Robotics, or Machine Learning.
- A minimum of five years of industry or research experience, with PhD experience applicable.
- Strong 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.
- Strong background in robotics including autonomy and/or manipulation.
- Experience with deploying artificial neural networks on hardware platforms.
- Ability to write production-level code in modern C++.
- Ability to prototype algorithms and train deep neural networks in Python.
Get some bonus points
- PhD degree in Robotics, Engineering, Computer Science, Machine Learning or a similar discipline, or an equivalent amount of research experience.
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Publications at top-tier conferences (e.g., ICRA, IROS, CoRL, RSS) specifically focusing on robotic manipulation, grasping, or contact-rich RL.
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Demonstrated experience working with tactile sensing, multi-fingered robotic hands, or bimanual manipulation.