Applied Scientist - Reinforcement learning, OMHS SCS
Core
Design, implement, and deploy reinforcement learning agents and control policies to optimize fulfillment center operations (throughput, flow, congestion) in production environments.
Role type
Applied Scientist (Reinforcement Learning)
Builds
Real-time MHE control systems and building-wide optimization solutions for fulfillment centers
Domain
Logistics / Supply Chain / Reinforcement Learning
Deliverable
production ML models
Required skills
Reinforcement Learning, Sequential Decision Making, Deep Learning, Python, Simulation Environments, Policy Optimization
Preferred skills
Robotics, Real-time Control, Industrial Process Optimization, Large-scale Production Deployment
Technologies
PyTorch, TensorFlow, Ray/RLlib, Gymnasium, Stable-Baselines3, Isaac Gym/Omniverse, MuJoCo
Responsibilities
Develop RL solutions for throughput and congestion control; Formulate operational problems as sequential decision-making tasks; Build high-fidelity simulation environments for training and validation; Integrate RL policies into real-time production systems.
Seniority
Senior, hands-on IC