Senior Applied Scientist , Prime Video Ads

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 subscriptions such as Apple TV+, HBO Max, Peacock, 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 team member, 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!

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.