Applied Scientist, Linear Personalization Experience Team (lpex)

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

Applied Scientist role focused on designing, developing, and deploying machine learning models for content recommendation and personalization at scale for Prime Video. The role involves owning the complete ML lifecycle, optimizing recommendation systems for real-time latency, conducting A/B experiments, and collaborating with engineering teams for production deployment and monitoring. The team is building next-generation AI-powered personalization and recommendation systems for the Linear TV experience.

What you'd actually do

  1. 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.
  2. Own the complete ML lifecycle: problem formulation, data analysis, feature engineering, model development, offline and online evaluation, and reliable production deployment.
  3. Build and continuously optimize recommendation systems with strict real-time latency requirements, ensuring that personalization decisions are delivered at speed and scale.
  4. Design and execute rigorous A/B and multivariate experiments to measure recommendation quality, understand causal drivers of engagement, and iterate rapidly toward customer impact.
  5. Partner with software engineering teams to productionize ML models, defining requirements for serving infrastructure, data pipelines, and model monitoring and observability.

Skills

Required

  • PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
  • 3+ years of building models for business application experience
  • Experience programming in Java, C++, Python or related language
  • Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

Nice to have

  • Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
  • Experience building complex recommendations or personalization systems that have been successfully delivered to customers at significant scale.

What the JD emphasized

  • building complex recommendations or personalization systems that have been successfully delivered to customers at significant scale

Other signals

  • design, develop, and deploy machine learning models for content recommendation
  • build and continuously optimize recommendation systems
  • design and execute rigorous A/B and multivariate experiments
  • partner with software engineering teams to productionize ML models