Applied Scientist II, RBS Tech

RBS (Retail Business Services) Tech team works towards enhancing the customer experience (CX) and their trust in product data by providing technologies to find and fix Amazon CX defects at scale. Our platforms help in improving the CX in all phases of customer journey, including selection, discoverability & fulfilment, buying experience and post‑buying experience (product quality and customer returns). The team also develops GenAI platforms for automation of Amazon Stores Operations. As a Sciences team in RBS Tech, we focus on foundational ML research and develop scalable state‑of‑the‑art ML solutions to solve the problems covering customer experience (CX) and Selling partner experience (SPX). We work to solve problems related to multi‑modal understanding (text and images), task automation through multi‑modal LLM agents, supervised and unsupervised techniques, multi‑task learning, multi‑label classification, aspect and topic extraction for customer anecdote mining, image and text similarity and retrieval using NLP and computer vision for product groupings and identifying duplicate listings in product search results. Key job responsibilities As an Applied Scientist, you will design and deploy scalable GenAI, NLP, and computer vision solutions that will impact the content visible to millions of customers and solve key customer experience issues. You will develop novel LLM, deep learning, and statistical techniques for task automation, text processing, image processing, pattern recognition, and anomaly detection problems. You will define the research and experiments strategy with an iterative execution approach to develop AI/ML models and progressively improve the results over time. You will partner with business and engineering teams to identify and solve large and significantly complex problems that require scientific innovation. You will help the team leverage your expertise by coaching and mentoring, contribute to the professional development of colleagues, and independently guide the team to file for patents and/or publish research work where opportunities arise. The RBS org deals with problems that are directly related to the selling partners and end customers, and the ML team drives resolution to organization‑level problems. The Applied Scientist role will impact large product strategy, identify new business opportunities, and provide strategic direction. Basic Qualifications PhD, or Master's degree 1 year of relevant applied research experience with PhD or 3+ years of applied research experience with a Masters’ degree in Electrical Engineering, Computer Science, Computer Engineering, Mathematics, or related field with specialization in Machine Learning, NLP, Computer Vision, Deep Learning, or GenAI related fields. Should be an expert in either Computer Vision or NLP and have a good understanding of the both. Excellent communication skills Preferred Qualifications Post Graduate degree (MS or PhD) in Electrical Engineering, Computer Science, Mathematics or Physics with specialization in ML, NLP or Computer Vision. Strong verbal/written communication skills, including an ability to effectively collaborate with both research and technical teams. Scientific thinking and the ability to invent, a track record of thought leadership and contributions that have advanced the field. Solid understanding of machine learning, deep learning algorithms and computational complexity. Strong CS fundamentals in data structures, problem solving, algorithm design and complexity analysis. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. #J-18808-Ljbffr