Applied Scientist, Grocery, Retail & In-Store Experience (GRAISE)

Amazon
Seattle, WA, USA2026-03-18ONSITE

About the job

The GRAISE team (Grocery, Retail & In-Store Experience) within Worldwide Grocery Store Tech (WWGST) builds foundational AI and machine learning systems that power Amazon's in-store grocery technologies. We develop domain-specific models that solve uniquely complex challenges in grocery — from smart shopping carts and inventory intelligence to personalization and store operations. Our mission is to create technology which makes grocery shopping more convenient, economical, personalized, and enjoyable for customers while empowering retailers with operational efficiency. We are looking for a talented and motivated Applied Scientist to join our team. In this role, you will design, develop, and deploy machine learning and computer vision models and algorithms that solve real-world problems at scale. You will work closely with engineering, product, and business teams to translate ambiguous problems into rigorous scientific solutions, and you will own the end-to-end development of models from ideation through production. This is a high-impact role where your work will directly shape the intelligence layer of Amazon's grocery ecosystem.

Responsibilities

Design and implement machine learning models to solve complex grocery-domain problems.

Conduct exploratory data analysis and develop deep understanding of domain-specific data challenges.

Collaborate with software engineers to productionize models and ensure reliability at scale.

Define and track key metrics to evaluate model performance and business impact.

Communicate findings and recommendations clearly to technical and non-technical stakeholders.

Stay current with the latest research and evaluate applicability to team problems.

Contribute to a culture of scientific rigor, experimentation, and continuous improvement.

Qualifications

Minimum

PhD, or Master's degree and 2+ years of CS, CE, ML or related field experience

2+ years of building machine learning models or developing algorithms for business application experience

Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

Experience with popular deep learning frameworks such as MxNet and Tensor Flow

Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning

Preferred

Experience in patents or publications at top-tier peer-reviewed conferences or journals

Experience with programming languages such as Python, Java, C++