Applied Scientist, Prime Video - Title Lifecycle Presentation

Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! The Prime Video Title Lifecycle Presentation team sits at the intersection of science, experimentation, and customer experience. We leverage data signals and rigorous testing to present the most engaging information about our content to customers at precisely the right moment. Our mission is to ensure every customer interaction with Prime Video content is informed, relevant, and compelling in order to drive discovery and engagement across our vast catalog. Every day, hundreds of millions of customers browse Prime Video. We already have a different team working on what to recommend, but this team is solving something harder: why should you watch it? The team optimizes the signals that convince a customer to press play. Think "Trending Now," "100% on Rotten Tomatoes," or "Most Watched This Week", the contextual cues that turn browsing into watching. Last year, by rigorously testing how we present these signals, we drove 116 million incremental streaming hours. Now we're building what comes next. We're creating an ML-powered platform that dynamically personalizes content signals for each customer, in each moment, across every surface (detail pages, homepages, carousels, and beyond). The core challenge: given a title, a customer, and a context, which signal (or combination of signals) maximizes the probability of engagement? This is not traditional ranking (we're not deciding which titles to show). We sit downstream of discovery, so once a title is surfaced, we determine the most compelling way to present it so the customer converts. Key job responsibilities As an Applied Scientist, you will have access to large datasets with billions of images and video to build large-scale machine learning systems. Additionally, you will analyze and model terabytes of text, images, and other types of data to solve real-world problems and translate business and functional requirements into quick prototypes or proofs of concept. We are looking for smart scientists capable of using a variety of domain expertise combined with machine learning and statistical techniques to invent, design, evangelize, and implement state-of-the-art solutions for never-before-solved problems. The ideal background would include: - Strong foundations in reinforcement learning (bandits, policy optimization) and/or representation learning (embeddings, multimodal models) - Experience with online experimentation and causal inference at scale - Familiarity with vision-language models, image embeddings, or content understanding