Applied Scientist Ii, Prime Video - Personalization and Discovery Science

Amazon Amazon · Big Tech · Seattle, WA · Machine Learning Science

Applied Scientist II at Amazon Prime Video focused on building and launching end-to-end AI solutions for personalization and discovery systems, utilizing deep learning, GenAI, and reinforcement learning. The role involves designing and conducting experiments to evaluate solutions and requires a strong background in machine learning model development and application.

What you'd actually do

  1. Develop AI solutions for various Prime Video Search systems using Deep learning, GenAI, Reinforcement Learning, and optimization methods
  2. Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end
  3. Design and conduct offline and online (A/B) experiments to evaluate proposed solutions based on in-depth data analyses
  4. Effectively communicate technical and non-technical ideas with teammates and stakeholders
  5. Stay up-to-date with advancements and the latest modeling techniques in the field

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 using Unix/Linux
  • Experience in professional software development

What the JD emphasized

  • building and guiding machine learning models from the ground up

Other signals

  • Develop AI solutions for various Prime Video Search systems using Deep learning, GenAI, Reinforcement Learning, and optimization methods
  • Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end
  • Design and conduct offline and online (A/B) experiments to evaluate proposed solutions based on in-depth data analyses