Applied Scientist - Computational Modeling, OMHS SCS

Amazon; Reading (MA); On-site; USD 136,000 - 184,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Parental leave

Job summary

Amazon is seeking an Applied Scientist on the Science SW team to serve as a versatile generalist, framing ambiguous business problems as tractable scientific problems and delivering ML systems from prototype to production across perception, optimization, and orchestration.

You will collaborate with researchers and engineers to prototype edge-deployable models, validate sensing hardware, and integrate solutions into scalable deployment architectures that improve fulfillment performance.

Qualifications

• PhD or master’s/PhD-equivalent in a quantitative field with strong ML/data science background.
• Experience with Python/Java/C++ in ML production environments.
• Strong foundations in statistics, optimization, and machine learning across domains.
• Experience with PyTorch or TensorFlow and scientific Python stack.

Responsibilities

• Own research and development of ML solutions across ML, CV, optimization, and physics-informed modeling in production.
• Ramp quickly on new domains by framing problems as tractable scientific questions and prototyping end-to-end.
• Prototype and evaluate edge-deployable models and sensing hardware under real-world constraints.
• Collaborate across science and engineering teams to integrate solutions into deployment architectures.

Skills

Python

Java

C++

Applied mathematics

ML theory

Education

Advanced degree in engineering/CS/quantitative field

Tools

PyTorch

TensorFlow

NumPy

SciPy

scikit-learn

pandas

Job description

As an Applied Scientist on the Science SW team, you will be a versatile generalist who collaborates closely with other scientists and engineers teams to bring research to production across a broad portfolio of problems: from computer-vision perception platforms to building-wide optimization and orchestration. This role combines the scientific application of ML and applied mathematics with a strong product focus. It will be your job to frame ambiguous business problems as tractable scientific problems, and to implement novel ML systems, first-principles models, embedded systems prototypes, and performance optimizations in both prototype and production environments.

Key job responsibilities

• Own the research and development of scientific and ML solutions across a broad range of problems spanning classical machine learning, statistical modeling, computer vision, optimization, and physics-informed / first-principles modeling in a production environment.
• Rapidly ramp on unfamiliar problem domains, frame ambiguous or open-ended business problems as tractable scientific problems, and prototype solutions end to end.
• Prototype and evaluate sensing hardware and lightweight, edge-deployable models that run on commodity compute under real-world constraints.
• Collaborate across multiple science and engineering teams to integrate your solutions into your deployment architecture.

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

• Currently has, or is in the process of obtaining, a Advanced degree in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
• Experience with programming languages such as Python, Java, C++
• Strong foundation in applied mathematics, statistics, and machine learning, with the versatility to work across multiple problem domains rather than a single specialization.
• Experience with popular deep learning frameworks (e.g., PyTorch, TensorFlow) and the scientific Python stack (e.g., NumPy, SciPy, scikit-learn, pandas).

Preferred Qualifications

• PhD with a demonstrated track record of solving problems across more than one domain (e.g., computer vision, statistical modeling, optimization, signal processing, controls, or physical modeling).
• Experience with computer vision and/or physics-informed and first-principles modeling.
• Hands-on hardware prototyping experience (sensors, cameras, embedded / edge compute) and experience optimizing models for resource-constrained hardware.
• Publications at peer-reviewed venues (e.g., CVPR, NeurIPS, ICML, ICLR, or the leading venues in the candidate's home discipline).

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.

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