Imitation Learning Engineer, RIVR CH
Amazon (AI roles) · Zurich, Zurich, CHE
No salary listed. Estimated from 1438 salary-disclosed Engineering roles on this board: $202K–$300K (interquartile range; estimate, not the employer's figure).
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RIVR, an Amazon company, is building Physical AI by deploying autonomous robots for real-world doorstep delivery. Operating daily in diverse urban environments, RIVR's robots continuously learn from and navigate the millions of scenarios encountered during deliveries. By owning the full stack from software to hardware, RIVR is purpose-built for safety, reliability, and the customer from day one.
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.
Key job responsibilities
Develop 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. - Master’s degree or higher in a relevant field such as Engineering, Robotics, or Machine Learning.
- 5 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.
- 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.
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.
Key job responsibilities
Develop 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. - Master’s degree or higher in a relevant field such as Engineering, Robotics, or Machine Learning.
- 5 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.
- 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.
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