Job Description

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

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.
• 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 three years of industry or research experience, with PhD experience applicable.
• Strong deep learning fundamentals including supervised learning, self-supervised learning, neural network architectures, policy optimization algorithms, imitation learning, and generative AI techniques, including Diffusion Models such as DALL-E 2 and Stable Diffusion, as well as Diffusion Policy.
• Strong background in robotics including autonomy and 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.

Amazon RIVR 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, we encourage you to apply and bring your unique self to our team.
We believe the best work is done when collaborating and therefore require in-person presence in our office locations.