Senior RL Engineer for Dexterous Robotic Manipulation

Amazon Inc.

Zürich

Hybrid

CHF 140.000 - 210.000

Vollzeit

vor 25 Stunden
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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

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.

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