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

About the job

We are looking for a talented Applied Scientist to join our team. In this role, you will design, develop, and deploy machine learning and computer vision models that solve real-world problems at scale in the Amazon grocery domain. You will work closely with engineering, product, and business teams to turn complex technical challenges into production-ready solutions, and own the model development lifecycle from experimentation through deployment. You will bring scientific rigor to every stage — from data analysis and model design to evaluation and iteration. This is a high-impact role where your models will directly improve the shopping experience for millions of customers in Amazon grocery stores.

Key job responsibilities Design, train, and evaluate computer vision and machine learning models for complex grocery-domain problems including product identification, shelf perception, and in-store scene understanding — iterating rapidly from prototype to production-quality solutions

Conduct rigorous exploratory data analysis to characterize domain-specific challenges (image variability, catalog gaps, label noise) and translate findings into actionable modeling decisions

Own the model development lifecycle from experimentation through deployment — collaborating with software and ML engineers to ensure models meet latency, throughput, and reliability requirements at production scale

Design and execute offline and online evaluation frameworks — defining metrics that capture both model performance and downstream business impact, and diagnosing failure modes to prioritize improvements

Build and improve data pipelines and annotation workflows that feed model training, including active learning strategies to maximize label efficiency

Communicate technical results, trade-offs, and recommendations clearly to engineering, product, and business stakeholders — connecting model behavior to customer experience outcomes

Stay current with state-of-the-art research in computer vision, multimodal learning, and representation learning — evaluating and adapting promising techniques to team-specific problems

Contribute to a culture of scientific rigor through reproducible experimentation, thorough documentation, peer code and design reviews, and raising the quality bar for the team

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.

About the team The GRAISE team (Grocery, Retail & In-Store Experience) within World Wide 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

Basic qualifications - 2+ years of solving business problems through machine learning, data mining and statistical algorithms experience - Experience in designing experiments and statistical analysis of results - Experience programming in Java, C++, Python or related language

Preferred qualifications - Experience working with large sets of data and deriving practical insights - 2+ years of deep learning, computer vision, human robotic interaction, algorithms implementation experience - Experience developing and evaluating data annotation and data quality metrics - Demonstrated ability to take a model from research prototype to production deployment — including performance profiling, latency optimization, and working within serving infrastructure constraints - Track record of clearly documenting experiments, writing technical design documents, and communicating results to cross-functional partners

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