(Senior) AI Engineer - Reinforcement Learning Manipulation, RIVR

Amazon Inc.; Zürich; Hybrid; CHF 140.000 - 210.000; Vollzeit

Zusammenfassung

Amazon seeks a Senior AI Engineer to advance reinforcement learning and deep learning for autonomous robots performing real-world doorstep deliveries. The role emphasizes learning from simulation and real data to refine dexterous manipulation, grasp synthesis, and end-effector control.

The candidate will address dynamic hand-offs, object interaction, and complex manipulation tasks while collaborating with the foundation model team and deploying models on hardware.

Qualifikationen

• Master's degree or higher in Engineering/Robotics or ML.
• 5+ years of industry or research experience; PhD applicable.
• Strong fundamentals in deep learning, RL, supervised and self-supervised learning.
• Robotics background with autonomy/manipulation experience.
• Production-level Python and system-level C++ coding.
• Experience deploying neural networks on hardware platforms.
• Experience with sim-to-real transfer and domain adaptation.

Aufgaben

• Develop RL algorithms for dexterous manipulation and robust end-effector control using multi-sensory inputs.
• Address real-world manipulation challenges such as varied packages and door-handling scenarios.
• Collaborate with the foundation model team to leverage simulated and real-world data.

Kenntnisse

Reinforcement Learning

Deep Learning

Python

C++

Robotics

MDPs

Sim-to-Real

Ausbildung

Master's degree or higher

PhD in Robotics/ML

Tools

TensorFlow

PyTorch

ROS

CUDA

Jobbeschreibung

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.

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.

Key job responsibilities

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.

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.

Collaborate with the foundation model team to innovate methods that leverage both simulated and real-world data.

Basic Qualifications

• 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.
• Strong background in robotic manipulation, including dynamics, grasp synthesis, and trajectory optimization.

Preferred Qualifications

• 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 (e.g., ICRA, IROS, CoRL, RSS) specifically focusing on robotic manipulation, grasping, or contact-rich RL.
• Demonstrated experience working with tactile sensing, multi-fingered robotic hands, or bimanual manipulation.

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