Applied Scientist - Computational Modeling, OMHS SCS

About the role

As an Applied Scientist on the Science SW team, you will be a versatile generalist who collaborates closely with other scientists and engineers 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. You will frame ambiguous business problems as tractable scientific problems and implement novel ML systems, first-principles models, embedded systems prototypes, and performance optimizations in both prototype and production environments.

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 our deployment architecture.

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 to ensure customers get 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 aligned experimentation, the team is dedicated to building self-optimizing fulfillment centers and developing 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, improving operational performance across the fulfillment network.

Requirements

• Currently has, or is in the process of obtaining, an advanced degree in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics, or an equivalent quantitative field.
• Experience with programming languages such as Python, Java, or 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 leading venues in the candidate's home discipline).

Pay

USA, MA (Boston, North Reading, Westboro) - $136,000.00 - $184,000.00 USD annually.

Benefits

• Sign-on payments and restricted stock units (RSUs).
• Comprehensive health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance, and optional supplemental life plans).
• Employee Assistance Program (EAP) and mental health support.
• Medical Advice Line and 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.