Are you excited about applying machine learning and applied mathematics to real-world systems at massive scale? As an Applied Scientist on this newly formed team, you will collaborate closely with scientists and engineers to bring research into production across a broad portfolio of problems — from computer vision perception platforms to building-wide optimization and orchestration. You will frame ambiguous business problems as tractable scientific challenges and implement novel machine learning (ML) systems, first-principles models, embedded systems prototypes, and performance optimizations in both prototype and production environments. This is a ground-floor opportunity to shape the scientific direction of a new organization, where your contributions will directly influence how Amazon's fulfillment network operates and evolves.
Key job responsibilities
- Design, develop, and deploy ML and scientific solutions spanning classical machine learning, statistical modeling, computer vision, optimization, and physics-informed modeling in production environments.
- Rapidly ramp on unfamiliar problem domains, frame ambiguous business problems as tractable scientific challenges, and prototype solutions end to end.
- Author or co-author research findings for internal or external peer-reviewed venues, and provide peer feedback on research procedures and results across teams.
- 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 deployment architecture, mentoring less experienced scientists along the way.
A day in the life
You might start your morning reviewing experiment results from an overnight model training run, then shift into a design discussion with engineers on how to deploy a new computer vision model to edge hardware in a fulfillment center. After lunch, you could be prototyping a physics-informed optimization approach, writing up findings for a research paper, or pairing with a teammate to debug a tricky data pipeline. As part of a new and growing organization, you will have a direct hand in shaping team practices, scientific roadmaps, and the tools you use every day.
About the team
Our team sits within Amazon's fulfillment technology organization and applies a range of scientific disciplines — including computer vision, optimization, reinforcement learning, and statistical modeling — to improve how goods move through Amazon's global fulfillment network. We build the models and systems that drive real-time orchestration, optimizing throughput, flow, and operational performance at scale.
As a newly formed organization, we are building our culture and scientific agenda from the ground up. You will join a collaborative, inclusive group of scientists and engineers who value experimentation, rigorous research, and delivering measurable impact for customers.
