Senior Applied Scientist, Linear Personalization Experience Team (lpex)

Amazon Amazon · Big Tech · Seattle, WA · Applied Science

Senior Applied Scientist on the Linear Personalization Experience (LPEX) team at Amazon Prime Video. This role focuses on defining and driving the science strategy and roadmap for Linear TV personalization, designing, developing, and deploying ML models for content recommendation, viewer engagement optimization, and real-time personalization. The role involves owning the complete ML lifecycle, building and optimizing recommendation systems with strict real-time latency requirements, and partnering with software engineering teams for production deployment. The team is building next-generation, AI-powered personalization and recommendation systems.

What you'd actually do

  1. Define and drive the science strategy and multi-year roadmap for Linear TV personalization, translating research advances into measurable business and customer experience outcomes.
  2. Design, develop, and deploy machine learning models for content recommendation, viewer engagement optimization, and real-time personalization at the scale of hundreds of millions of Prime Video customers.
  3. Own the complete ML lifecycle: problem formulation, data analysis, feature engineering, model development, offline and online evaluation, and reliable production deployment.
  4. Build and continuously optimize recommendation systems with strict real-time latency requirements, ensuring that personalization decisions are delivered at speed and scale.
  5. Design and execute rigorous A/B and multivariate experiments to measure recommendation quality, understand causal drivers of engagement, and iterate rapidly toward customer impact.

Skills

Required

  • PhD, or Master's degree and 6+ years of applied research experience
  • 5+ years of building machine learning models for business application experience
  • Experience programming in Java, C++, Python or related language
  • Experience with neural deep learning methods and machine learning

Nice to have

  • Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
  • Experience with large scale distributed systems such as Hadoop, Spark etc.
  • Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
  • Experience with AWS services including S3, Redshift, Sagemaker, EMR, Kinesis, L

What the JD emphasized

  • end-to-end ownership of the product
  • real-time personalization
  • strict real-time latency requirements
  • production deployment
  • large scale distributed systems
  • state-of-the-art deep learning models architecture design

Other signals

  • design, develop, and deploy machine learning models for content recommendation
  • Build and continuously optimize recommendation systems with strict real-time latency requirements
  • productionize ML models
  • large scale distributed systems
  • state-of-the-art deep learning models architecture design