# Research Scientist II, Grocery ECO at Amazon

Amazon is hiring a Research Scientist II, Grocery ECO in Bengaluru, India. Level rates it Builds AI ●●●●; you can [apply on Amazon](https://jobsbylevel.com/go/9ba12657-5c64-4bba-b1b7-2234523a51f0?utm_medium=ai-agent).

AI Level 4, AI centrality 90 out of 100. IN, KA, Bengaluru.

## Details

- Company: [Amazon](https://jobsbylevel.com/companies/amazon)
- AI level: AI Level 4 (score 90 out of 100)
- Location: IN, KA, Bengaluru
- Posted: November 6, 2026
- Apply: https://jobsbylevel.com/go/9ba12657-5c64-4bba-b1b7-2234523a51f0?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=research-scientist-ii-grocery-eco-at-amazon-16e56b

## Prepare for this job

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## Description

We are looking for a talented Research 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 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 a Research Scientist on the GRAISE team, you'll spend your days analyzing model performance from overnight experiments, 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 - PhD, or Master's degree - Experience investigating the feasibility of applying scientific principles and concepts to business problems and products - Experience analyzing both experimental and observational data sets - 3+ years of building machine learning models for business application experience - Experience programming in Java, C++, Python or related language - Experience with neural deep learning methods and machine learning Preferred qualifications - Knowledge of R, MATLAB, Python or similar scripting language - 3+ years of applied research experience - Knowledge of deep learning, machine learning and statistics - Experience in patents or publications at top-tier peer-reviewed conferences or journals Our inclusive

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