Applied Scientist: Real-Time Optimization & RL for Scheduling

Socket.dev; Boston (MA); On-site; USD 143,000 - 193,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Parental leave

Job summary

Amazon in Boston, MA is seeking an Applied Scientist to advance optimization and sequential decision-making for production systems. You will design, implement, and deploy novel decision policies and optimization models, with rigorous evaluation before fleet deployment.

You will collaborate with researchers and engineers across teams, publish when warranted, and contribute to building self-optimizing fulfillment centers and real-time control. A PhD and strong ML background are expected.

Qualifications

• PhD in Operations Research, Statistics, Applied Mathematics, Engineering, Computer Science or related field.
• 2+ years building ML models or algorithms for business applications.
• Experience with optimization mathematics such as LP and nonlinear optimization.
• Python scripting proficiency.
• Experience with end-to-end ownership of major deliverables.
• Experience with DL frameworks and RL tooling (PyTorch, RLlib, Gymnasium).

Responsibilities

• Own research and development of optimization and sequential decision-making solutions spanning constraint programming, stochastic and robust optimization, contextual bandits, and reinforcement learning for real-time MHE control and scheduling optimization in a production environment.
• Formulate fulfillment operations and manufacturing scheduling problems as optimization or sequential decision-making problems, and design multi-objective functions that balance competing operational objectives such as on-time delivery, utilization, changeover cost, and schedule stability.
• Build and leverage high-fidelity simulation and emulation environments for safe offline training, policy validation, and transfer to live systems before fleet-scale deployment.
• Collaborate across multiple science and engineering teams to integrate policies into production planning and real-time control systems, including monitoring, guardrails, and staged rollout.
• Communicate results and their limitations clearly in writing to technical and business audiences, and contribute to the team's external research presence through publication where the work merits it.

Skills

ML modeling

Optimization

Reinforcement learning

Python

End-to-end ownership

RL tooling

Education

PhD in Operations Research/Statistics/Applied Math/Engineering/CS

Tools

PyTorch

d3rlpy

Ray RLlib

Gymnasium

Stable-Baselines3

Isaac Gym/Omniverse

Job description

Amazon in Boston, MA is seeking an Applied Scientist to advance optimization and sequential decision-making for production systems. You will design, implement, and deploy novel decision policies and optimization models, with rigorous evaluation before fleet deployment.

You will collaborate with researchers and engineers across teams, publish when warranted, and contribute to building self-optimizing fulfillment centers and real-time control. A PhD and strong ML background are expected.