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.
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
- 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 a setting analogous to ours: real-time control, robotics or material handling, industrial process or operations.
- Experience deploying RL or ML models to production at scale and partnering with engineering teams on real-time inference and feedback loops.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, 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 https://amazon.jobs/en/benefits.
USA, MA, Boston - 142,800.00 - 193,200.00 USD annually
USA, MA, N.Reading - 142,800.00 - 193,200.00 USD annually
