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Amazon RIVR

Posted 21 months ago

Open

AI Engineer - Reinforcement Learning (Senior)

ZürichOn-siteFull-time

AI Summary

Senior AI Engineer leads RL and deep learning initiatives for autonomous robotics, guiding a software team and collaborating across computer vision and imitation learning to apply simulated and real-world data to real-world locomotion and manipulation challenges.

About this role

Amazon RIVR 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, Amazon RIVR is building the next generation of safe, reliable autonomous robots for last-mile delivery


Job Description
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, to lead our engineering team. Your role will involve leveraging both simulated and real-world data to address practical challenges. 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

  • Develop cutting-edge reinforcement learning algorithms to enable robots to autonomously execute motor commands based on raw sensor input.
  • Design, test, and refine your algorithms to meet the demands of complex real-world locomotion, autonomy and manipulation tasks.
  • Collaborate with the computer vision and imitation learning team to innovate methods that leverage both simulated and real-world data.
  • Implement deployment-ready code for the real robot, optimized for the robot’s computational constraints.
  • Build, lead and mentor an exceptional team of software engineers.
  • Provide expert guidance to product managers and executives for strategic decision-making.
  • Create and maintain documentation, guidelines, and best practices to streamline knowledge sharing.

    • What you must have

  • 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.
  • Publications at top-tier conferences.
  • Experience in managing a software team.

    • Skills

    Code Optimization For Embedded SystemsDeep LearningDomain AdaptationHardware Deployment Of Neural NetworksManipulationMDPsModel-based RLModel-free RLNeural Network ArchitecturesPolicy OptimizationProduction-level C++PythonReinforcement LearningRobotics AutonomyRobotics Software DevelopmentRobot Perception IntegrationSelf-supervised LearningSim-to-real TransferSupervised LearningTraining On Simulated DataTransfer Learning

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