Applied RL Scientist - Real-Time Fulfillment Optimization

Amazon; Reading (MA); On-site; USD 143,000 - 193,000; Full time

Benefits offered by this job

RSUs

Health insurance

401(k) matching

Parental leave

Job summary

Amazon is seeking an Applied Scientist on the Science SW team to bring RL research to production. You will design, implement, and deploy novel RL agents and control policies in production environments, with emphasis on high-fidelity simulation and real-time integration.

Collaborate across science and engineering teams to scale RL solutions for fulfillment operations, real-time MHE control, and fleet-scale deployment, delivering measurable improvements in throughput and system performance.

Qualifications

• PhD in computer science, machine learning, engineering, or related fields.
• 2+ years of building machine learning models or developing algorithms for business application experience
• Demonstrated experience developing and applying reinforcement learning and sequential decision-making methods to real-world problems.
• Fluency in Python; C++ is a plus.

Responsibilities

• Own the research and development of reinforcement learning and sequential decision making solutions spanning deep RL, policy optimization, offline/batch RL, contextual bandits, and multi-agent RL for real-time MHE control and building-wide optimization in a production environment.
• Formulate fulfillment operations problems as sequential decision-making problems and design multi-objective reward functions.
• Build high-fidelity simulation environments for offline training, policy validation, and sim-to-real transfer before deployment.
• Collaborate across multiple science and engineering teams to integrate RL policies into real-time production and control systems.

Skills

PhD in CS/ML/Engineering

End-to-end research ownership

Strong problem solving

Education

PhD in computer science, machine learning, engineering, or related fields

Tools

PyTorch

TensorFlow

Ray/RLlib

Gymnasium

MuJoCo

Job description

Amazon is seeking an Applied Scientist on the Science SW team to bring RL research to production. You will design, implement, and deploy novel RL agents and control policies in production environments, with emphasis on high-fidelity simulation and real-time integration.

Collaborate across science and engineering teams to scale RL solutions for fulfillment operations, real-time MHE control, and fleet-scale deployment, delivering measurable improvements in throughput and system performance.