Applied Scientist - Reinforcement learning, OMHS SCS

Description

• As an Applied Scientist on the Science SW team, you will collaborate closely with other scientists and engineers to bring Reinforcement Learning (RL) research to production. This role combines the scientific

Description

• As an Applied Scientist on the Science SW team, you will collaborate closely with other scientists and engineers to bring Reinforcement Learning (RL) research to production. This role combines the scientific application of ML, and specifically RL and sequential decision making, with software development engineering and a strong product focus. It will be your job to design, implement, and deploy novel RL agents, reward models, and control policies in both prototype and production environments, and to prove their impact in high-fidelity simulation before scaling them across the fleet. Key job 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 (throughput optimization, flow, merge, and congestion control) as sequential decision-making problems, and design multi-objective reward functions that balance competing operational objectives. • Build and leverage high-fidelity simulation environments for safe offline training, policy validation, and sim-to-real transfer before fleet-scale deployment. • Collaborate across multiple science and engineering teams to integrate RL policies into real-time production and control systems. About the team Amazon is building next generation software, hardware, and processes that will run our global network of fulfillment centers that move millions of units of inventory, and ensure customers get what they want when promised. The Science Software team in the One MHS organization unlocks Material Handling Equipment (MHE) innovation through a multiplicity of disciplines within Artificial Intelligence (AI) and applied science, including Computer Vision (CV), Physics-Informed Neural Networks (PINNs), Optimization, Reinforcement Learning, classical Machine Learning, statistical modeling, and sensing-hardware prototyping. Rooted in first principles aligned experimentation, the team is dedicated to building self-optimizing fulfillment centers, developing the models that drive real-time, building-wide orchestration of MHE. We conduct experiments, develop models, and apply machine learning (ML) at scale to optimize throughput, flow, merge, and congestion control, and to improve operational performance across the fulfillment network.

Basic 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 (e.g., deep RL, policy gradient / actor-critic methods, offline RL, contextual bandits, or multi-agent RL) to real-world control or optimization problems. - Fluency in a high-level programming language such as Python; experience with C++ is a plus. - Experience with popular deep learning frameworks (e.g., PyTorch, TensorFlow) and RL tooling or simulators (e.g., Ray/RLlib, Gymnasium, Stable-Baselines3, Isaac Gym/Omniverse, MuJoCo). - Ownership of end-to-end solutions in terms of research, prototyping, and experimentation.

Preferred Qualifications