AmazonPosted 1mo ago

Applied Scientist - Reinforcement learning, OMHS SCS

Applied Scientist - Reinforcement learning, OMHS SCS at Amazon scores 99 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.

US, MA, N.readingmidfull-time$143k-$193k

AI in this role

pytorchtensorflow

computer-vision

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

- 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.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

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

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Computer VisionPyTorchTensorFlow

Questions you could be asked

Walk me through a computer vision problem you solved, from raw data to a deployed model.
Walk me through how you've used PyTorch in your day-to-day work.
What are the limits of TensorFlow that you've run into, and how did you work around them?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: Computer Vision, PyTorch, and TensorFlow. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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