Applied Scientist, Prime Video - Title Lifecycle Presentation

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

Applied Scientist role at Amazon Prime Video focused on building machine learning systems for content presentation and discovery. The role involves working with multi-modal embeddings, contextualized ranking, reinforcement learning, and recommender systems to improve customer engagement with the Prime Video catalog.

What you'd actually do

  1. As an Applied Scientist, you will have access to large datasets with billions of images and video to build large-scale machine learning systems.
  2. Additionally, you will analyze and model terabytes of text, images, and other types of data to solve real-world problems and translate business and functional requirements into quick prototypes or proofs of concept.
  3. We are looking for smart scientists capable of using a variety of domain expertise combined with machine learning and statistical techniques to invent, design, evangelize, and implement state-of-the-art solutions for never-before-solved problems.

Skills

Required

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

Nice to have

  • Experience using Unix/Linux
  • Experience in professional software development

What the JD emphasized

  • Multi-modal embeddings for rich metadata representation, enabling nuanced understanding of content attributes and customer preferences
  • Contextualized ranking systems that adapt to customer intent, viewing context, and real-time signals
  • Reinforcement learning frameworks that create continuous improvement loops, allowing our systems to learn and optimize from customer interactions over time
  • Recommender systems experience, with proven ability to build and scale personalization solutions

Other signals

  • building sophisticated machine learning systems
  • multi-modal embeddings
  • contextualized ranking systems
  • reinforcement learning frameworks
  • recommender systems