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

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.

Key job 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.

A day in the life
As an Applied Scientist on the GRAISE team, you'll spend your days analyzing model performance from overnight experiments, collaborating with engineers to deploy computer vision models to production, and prototyping new approaches using multimodal learning with store video and sensor data. You'll present findings to product and business stakeholders, translating technical results into actionable recommendations. Throughout the day, you'll balance rigorous scientific thinking with practical engineering constraints, knowing your work directly improves the shopping experience for millions of customers in Amazon grocery stores.