Applied Scientist, Grocery, Retail & In-Store Experience (GRAISE)
Amazon · Seattle, WA · Data Science
About this role
Amazon is hiring a mid-level Applied Scientist in the machine learning function based in Seattle, WA. The posting calls out experience with Python, Java, C++, TensorFlow and roughly 2+ years of relevant work. Listed education preference: a master's degree or equivalent. Compensation is listed at $142,800–$193,200 per year.
- Role
- Applied Scientist
- Function
- machine learning
- Level
- mid
- Track
- Individual contributor
- Employment
- Full-time
- Location
- Seattle, WA
- Experience
- 2+ years
- Education
- Master's degree
- Department
- Data Science
- Posted
- Mar 18, 2026
More roles at Amazon
Job description
from Amazon careersThe 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…