Applied Scientist - Simulation and Large-Scale RL, Amazon Robotics - Vulcan Stow
Core
Design and train reinforcement learning policies for non-prehensile and contact-rich manipulation, building simulation environments and sim-to-real methods to deploy robots that handle diverse items at Amazon scale.
Role type
Applied Scientist (Reinforcement Learning & Robotics)
Builds
Scalable simulation environments, training curricula, reward formulations, and sim-to-real transfer methods for robotic manipulation.
Domain
Robotics, Reinforcement Learning, Simulation
Deliverable
production ML models
Required skills
Reinforcement learning, simulation environment design, sim-to-real transfer, reward formulation, Python/C++/Java programming, robotics control, academic publication experience
Preferred skills
Professional software development, learned dynamics, world models for manipulation
Technologies
Python, C++, Java, simulation frameworks
Responsibilities
Design and train RL policies for manipulation, build and scale simulation environments, develop sim-to-real methods, write production-quality code for training pipelines, evaluate policy behavior and failure modes, partner with control/perception/hardware teams, represent organization in academia
Seniority
Senior, hands-on IC