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

We are looking for a talented Applied Scientist to join our 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 youll 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. Youll present findings to product and business stakeholders translating technical results into actionable recommendations. Throughout the day youll 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 Amazons 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

- 3 years of building models for business application experience
- PhD or Masters degree
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience programming in Java C Python or related language

- 2 years of hands-on experience building and deploying computer vision models (object detection image classification segmentation or visual search) on real-world data beyond academic benchmarks
- Demonstrated ability to take a model from research prototype to production deployment including performance profiling latency optimization and working within serving infrastructure constraints
- Practical experience with noisy imperfect or sparse training data including techniques such as semi-supervised learning active learning weak supervision or synthetic data generation
- Experience building or contributing to annotation pipelines and human-in-the-loop workflows that improve data quality and labeling efficiency over time
- Track record of clearly documenting experiments writing technical design documents and communicating results to cross-functional partners

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