Are you passionate about building intelligent systems that personalize experiences for hundreds of millions of customers? Do you want to work at the intersection of deep learning, causal inference, and product optimization, where your models directly shape how customers experience advertising on one of the world's largest streaming platforms? Prime Video Ads is redefining what ad-supported streaming looks like. We launched ads on Prime Video in 2024 and are now in the process of building the science foundation to make our ad experience the most customer-centric in the industry. Unlike traditional digital advertising where clicks and conversions provide direct feedback, streaming presents unique scientific challenges. Indirect signals, heterogeneous customer responses, and the need to balance monetization with long-term engagement, all at massive scale. Key job responsibilities The Science Our team tackles problems that span the full ML lifecycle, from exploratory research and offline modeling to online experimentation and production deployment. The science challenges include: * Heterogeneous customer responses. The same customer responds differently to ads depending on what they're watching, how engaged they are, and their broader streaming context. We need to understand and predict this variation to make better advertising decisions in real time. * Signal sparsity in streaming. Unlike search or retail advertising, streaming offers no clicks, no conversions, and limited direct feedback. We must develop creative approaches to infer customer preferences, intent, and tolerance from indirect behavioral signals. * Personalization at scale. A one-size-fits-all ad experience leaves value on the table for both customers and the business. We build systems that adapt ad load, placement, and content to individual viewers across 100M+ customers and 100k+ titles. * Small effects, large variance. Ad interventions typically produce 0.1-2% shifts in engagement, effects easily overwhelmed by natural behavioral variance. Measuring, attributing, and optimizing these small signals requires rigorous experimental design and causal methodologies. * Competing objectives. Revenue, customer engagement, long-term retention, and advertiser value are in tension. We develop principled frameworks to navigate these tradeoffs and optimize for sustainable outcomes rather than any single metric. A day in the life * Lead the research and development of ML models that personalize advertising decisions for 100M+ customers across 100k+ titles, with production deployment in mind * Develop deep learning architectures (multi-task learning, embedding-based representations) for customer behavior prediction at scale * Design and analyze large-scale A/B experiments, applying causal inference techniques to measure and optimize the impact of ad strategies on customer engagement and monetization Partner with engineering to ensure models meet production latency and scalability requirements * Collaborate with product managers to frame business problems as tractable ML problems and translate findings into product decisions * Shape the team's scientific roadmap, identifying high-impact research directions About the team The PV Ad CX team's mission is to create the world's most customer-centric ad experience for video streaming. We build adaptive systems that determine when, how many, and what ads to show each customer, personalized to their viewing behavior, content context, and engagement patterns. We aspire to transform ad breaks from interruptions into moments that feel relevant and thoughtful. BASIC QUALIFICATIONS - 3+ years of building machine learning models for business application experience - PhD, or Master's degree and 6+ years of applied research experience - Experience programming in Java, C++, Python or related language - Experience with neural deep learning methods and machine learning PREFERRED QUALIFICATIONS - Experience with large scale distributed systems such as Hadoop, Spark etc. - Experience working with PyTorch or JAX software, or experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution 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, NY, New York - 183,800.00 - 248,700.00 USD annually USA, VA, Arlington - 167,100.00 - 226,100.00 USD annually USA, WA, SEATTLE - 167,100.00 - 226,100.00 USD annually