Software Development Engineer - Prime Video, Linear Personalization Experience Team (lpex)

Amazon Amazon · Big Tech · Seattle, WA · Software Development

Software Development Engineer role focused on building and optimizing AI-powered personalization and recommendation systems for Prime Video's Linear TV segment. The role involves designing and implementing high-performance personalization systems, productionizing ML models with Applied Scientists, architecting data pipelines, developing real-time serving infrastructure, and leading A/B testing frameworks. Requires strong software development skills, experience with ML frameworks, SQL, distributed systems, and knowledge of recommendation systems and model serving.

What you'd actually do

  1. Design and implement high-performance personalization systems that scale to millions of users and real-time content decisions
  2. Collaborate with Applied Scientists to productionize ML models for content recommendation and viewer engagement optimization
  3. Architect and build data pipelines that process viewer behavior, content metadata, and real-time signals
  4. Develop and optimize real-time serving infrastructure for recommendation models with strict latency requirements
  5. Lead the technical design and implementation of A/B testing frameworks to measure and improve recommendation quality

Skills

Required

  • Java/Python
  • ML frameworks (TensorFlow, PyTorch)
  • SQL
  • distributed systems
  • AWS services (DynamoDB, SQS, SageMaker, Lambda)
  • recommendation systems
  • personalization algorithms
  • model serving infrastructure

Nice to have

  • multi-task
  • quickly adapt to new development environments
  • changing business requirements
  • learn new systems
  • create reliable/maintainable code
  • creative and scalable solutions
  • communicate clearly and concisely

What the JD emphasized

  • productionize ML models
  • recommendation systems
  • personalization algorithms
  • model serving infrastructure
  • real-time serving infrastructure

Other signals

  • productionize ML models
  • recommendation systems
  • personalization algorithms
  • real-time serving infrastructure