AI Engineer - Imitation Learning (Senior)

النص مقدّم من المصدر. تحقّق من الشروط الكاملة في الإعلان الأصلي.

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 Imitation learning enables our robot to mimic "expert" behaviors, derived from human demonstrations or algorithmic strategies.

By utilizing state-of-the-art generative AI and similar methods, our wheeled-legged robot can significantly enhance its autonomy and manipulation skills. In this role, you will enable robots to autonomously generate actions from demonstrations and real-time sensor data. These processes may also incorporate responses to natural language commands, further advancing the robot's skills.

We are seeking an expert in imitation learning and generative AI techniques that directly produce robot behaviors, along with a deep knowledge of both supervised and self-supervised learning algorithms. If you are passionate about pushing the boundaries of AI and eager to deliver innovative solutions, we invite you to join us in shaping the future of intelligent robotics.

What you’ll be doing Develop cutting-edge imitation learning algorithms, such as diffusion policies, to enable robots to autonomously execute actions based on demonstrations and real-time sensor data. Design, test, and refine your algorithms to meet the demands of complex real-world autonomy and manipulation tasks. Construct a dataset for the imitation learning algorithm using human demonstrations or automated expert algorithms.

Streamline the workflow to efficiently expand the imitation learning dataset with new tasks. Collaborate with the reinforcement 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.