Applied Scientist - Reinforcement learning, OMHS SCS

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—specifically RL and sequential decision making—with software development engineering and a strong product focus.

About the role

You will design, implement, and deploy novel RL agents, reward models, and control policies in both prototype and production environments. You will prove their impact in high-fidelity simulation before scaling them across the fleet.

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, moving millions of units of inventory and ensuring customers receive what they want when promised. The Science Software team in the One MHS organization unlocks Material Handling Equipment (MHE) innovation through 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 experimentation, the team is dedicated to building self-optimizing fulfillment centers, developing models that drive real-time, building-wide orchestration of MHE. We conduct experiments, develop models, and apply machine learning at scale to optimize throughput, flow, merge, and congestion control, improving operational performance across the fulfillment network.

Requirements

• PhD in computer science, machine learning, engineering, or related fields.
• 2+ years of building machine learning models or developing algorithms for business applications.
• 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

• First-author publications at top-tier machine learning and AI venues (e.g., NeurIPS, ICML, ICLR, AAAI, AISTATS, CoRL, or RLC/RLDM).
• Experience applying RL in settings analogous to ours: real-time control, robotics, material handling, industrial processes, or operations.
• Experience deploying RL or ML models to production at scale and partnering with engineering teams on real-time inference and feedback loops.

Pay

Base salary range: $142,800 – $193,200 USD annually (locations: Boston, MA; North Reading, MA). Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on experience, qualifications, and location.

Benefits

• Health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance, and optional supplemental life plans).
• Employee Assistance Program (EAP) and mental health support, including a medical advice line.
• Flexible Spending Accounts.
• Adoption and surrogacy reimbursement coverage.
• 401(k) matching.
• Paid time off and parental leave.

Learn more about our benefits at amazon.jobs/en/benefits.