Prime Video is disrupting traditional media with an ever-increasing selection of movies, TV shows, Emmy Award winning original content, add-on subscriptions including HBO and Showtime, and live events like Thursday Night Football. Within this expanding ecosystem, Linear TV (24/7 Television or broadcast programming) has emerged as one of our fastest-growing segments, with viewership hours increasing significantly year over year. This growth demonstrates that even in the streaming era, customers deeply value the lean-back, curated experience that Linear TV provides. Our data shows that Linear TV viewers develop strong habitual viewing patterns, spending more time on the platform and engaging more consistently than traditional VOD-only users. While video on demand continues to grow, the predictable nature of scheduled programming creates daily viewing rituals and higher customer retention. We in Linear Personalization are building next-generation AI-powered personalization and recommendation systems to enhance this natural engagement and provide a best-in-class Linear TV experience for Prime Video customers. By understanding and adapting to these viewing habits, we can create more compelling and sticky experiences for our customers. It's Day 1 for personalizing linear TV experience on Prime Video, and you'll be at the forefront of this innovation.

Design and implement high-performance personalization systems that scale to millions of users and real-time content decisions

Collaborate with Applied Scientists to productionize ML models for content recommendation and viewer engagement optimization

Architect and build data pipelines that process viewer behavior, content metadata, and real-time signals

Develop and optimize real-time serving infrastructure for recommendation models with strict latency requirements

Lead the technical design and implementation of A/B testing frameworks to measure and improve recommendation quality

Define system architecture and implement specific components while considering current and future technology choices

Define and drive software best practices, including coding standards, code reviews, and testing

Coach and mentor engineers on the team to foster a supportive culture of collaboration, scalability, and performance.

Lead projects that require collaboration with multiple engineers, product managers, applied scientists, and cross-functional teams.