Applied Scientist Ii, Amazon Search

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

Applied Scientist II role focused on developing and improving large-scale machine learning systems for Amazon Search, specifically in ranking, reinforcement learning, and LLMs to enhance the customer shopping experience. The role involves building systems that generate content and assemble coherent page experiences, directly impacting millions of customers.

What you'd actually do

  1. Apply state-of-the-art Machine Learning (ML) algorithms, including Deep Learning, Reinforcement Learning, and Large Language Models (LLMs), to improve hundreds of millions of customers' shopping experience.
  2. Have measurable business impact using A/B testing.
  3. Work in a dynamic team that provides continuous opportunities for learning and growth.
  4. Work with leaders in the field of machine learning.

Skills

Required

  • 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
  • 1+ years of building machine learning models or developing algorithms for business application experience
  • 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

  • 1+ years of building production software experience
  • Ph.D. in computer science, mathematics, statistics, machine learning or equivalent quantitative field
  • Experience in written and verbal communication with the ability to present complex technical information in a clear and concise manner to executives and non-technical leaders
  • At least 2 years of experience with predictive modeling and analysis, applying various machine learning techniques including supervised/unsupervised learning, deep learning, and reinforcement learning
  • Strong publication record at top ML conferences and journals

What the JD emphasized

  • building machine learning models or developing algorithms for business application experience
  • building production software experience
  • predictive modeling and analysis, applying various machine learning techniques including supervised/unsupervised learning, deep learning, and reinforcement learning
  • Strong publication record at top ML conferences and journals

Other signals

  • large-scale machine learning systems
  • ranking, reinforcement learning, and large language models (LLMs)
  • next generation Amazon shopping experience
  • millions of customers
  • generating relevant content with LLMs
  • assembling a whole page experience that is coherent, dynamic, and interesting
  • improve our ranking and optimization algorithms
  • driving features from idea to deployment