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
BASIC QUALIFICATIONS
- 3+ years of building models for business application experience
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- Experience programming in Java, C++, Python or related language
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
PREFERRED QUALIFICATIONS
- Experience using Unix/Linux
- Experience in professional software development
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, WA, SEATTLE - 142,800.00 - 193,200.00 USD annually
