Principal Technical Program Manager - Prime Video, Pv Personalization and Discovery

Amazon Amazon · Big Tech · Seattle, WA · Project/Program/Product Management--Technical

Principal Technical Program Manager for Prime Video's Personalization and Discovery teams, focusing on the technical strategy and architecture of the end-to-end personalization stack, including ML/AI infrastructure, recommender systems, and generative AI applications. The role involves leading the delivery of scalable, science-friendly infrastructure to accelerate innovation in ML and AI for customer engagement, retention, and long-term value.

What you'd actually do

  1. Consistently meet challenging deadlines and deliver high-quality solutions that enable the Prime Video platform to scale to meet growing customer demand.
  2. Overcome obstacles and persist in the face of setbacks to ensure critical initiatives are successfully completed.
  3. Proactively identify and mitigate risks, communicating regularly with stakeholders on project status and issues.
  4. Demonstrate deep technical expertise in cloud infrastructure, distributed systems, ML/AI fluency and large-scale data processing.
  5. Understand the architecture and design limitations of the existing Prime Video systems, ML models, and drive technical decisions that address deficiencies and optimize performance.

Skills

Required

  • 7+ years of working directly with engineering teams experience
  • 7+ years of technical product or program management experience
  • 5+ years of software development experience
  • Experience designing or architecting (design patterns, reliability and scaling) of new and existing systems
  • Experience managing programs across cross functional teams, building processes and coordinating release schedules
  • Experience owning/driving roadmap strategy and definition

Nice to have

  • 8+ years of hands-on work managing complex technology projects experience
  • Experience managing projects across cross functional teams, building sustainable processes and coordinating release schedules

What the JD emphasized

  • ML/AI fluency
  • large-scale data processing
  • ML models

Other signals

  • ML/AI infrastructure
  • Recommender Systems
  • personalization
  • Generative AI