The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through industry leading generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. The Demand Utilization team with Sponsored Products and Brands owns finding the appropriate ads to surface to customers when they search for products on Amazon. We strive to understand our customers’ intent and identify relevant ads which enable them to discover new and alternate products. This also enables sellers on Amazon to showcase their products to customers, which may at times be buried deeper in the search results. Our systems and algorithms operate on one of the world's largest product catalogs, matching shoppers with products - with a high relevance bar and strict latency constraints. We are a team of machine learning scientists and software engineers working on complex solutions to understand the customer intent and present them with ads that are not only relevant to their actual shopping experience, but also non-obtrusive. This area is of strategic importance to Amazon Retail and Marketplace business, driving long term-growth. We are looking for an Applied Scientist II, with a strong background in Machine Learning and Generative AI to optimize serving ads on billions of product pages. The solutions you create would drive step increases in coverage of sponsored ads across the retail website and ensure relevant ads are served to Amazon's customers. You will directly impact our customers' shopping experience while helping our sellers get the maximum ROI from advertising on Amazon. You will be expected to demonstrate strong ownership and should be curious to learn and leverage the rich textual, image, and other contextual signals. This role will challenge you to utilize innovative machine learning techniques in the domain of predictive modeling, natural language processing (NLP), deep learning, reinforcement learning, query understanding, vector search, image recognition, and multi-modal AI to deliver significant impact for the business. In addition, you will be at the forefront of leveraging Generative AI (GenAI) technologies, including Large Language Models (LLMs) and foundation models, to drive advanced language understanding, creative ad content generation, and retrieval-augmented generation (RAG). You will also design and build agentic AI systems capable of autonomous, multi-step reasoning, tool use, and chain-of-thought decision-making, while applying techniques such as prompt engineering, fine-tuning, RLHF (Reinforcement Learning from Human Feedback), and embedding-based retrieval to develop scalable, production-grade solutions. Ideal candidates will have hands-on experience fine-tuning, evaluating, and deploying LLMs at scale, along with a strong understanding of emerging GenAI paradigms including agentic workflows and responsible AI practices. You should be able to work cross-functionally across multiple stakeholders, synthesize the science needs of our business partners, develop models to solve business needs, and implement solutions in production. In addition to being a strongly motivated IC, you will also be responsible for mentoring junior scientists, guiding them to deliver high-impact products and services for Amazon customers and sellers, and fostering a culture of innovation around the latest advancements in Generative AI and LLM technologies. Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. Team video https://youtu.be/zD_6Lzw8raE Key job responsibilities As an Applied Scientist II on this team, you will: - Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity. - Perform hands-on analysis and modeling of enormous data sets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience. - Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in deploying your ML models. - Run A/B experiments, gather data, and perform statistical analysis. - Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. - Research new and innovative machine learning approaches.
