Applied Scientist, Sponsored Products

Amazon Amazon · Big Tech · Arlington, VA · Applied Science

The Sponsored Products and Brands team at Amazon Ads is seeking an Applied Scientist to leverage generative AI and machine learning to revolutionize advertising experiences. This role involves driving end-to-end ML projects, analyzing large datasets, building and deploying models into production, and establishing scalable processes for ML development and serving. The focus is on creating innovative ad tech and shopping experiences off the store, working with external and internal partners to connect systems, understand customers, and drive results at scale, with a GenAI-first approach.

What you'd actually do

  1. Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity.
  2. Perform hands-on analysis and modeling of enormous data sets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience.
  3. Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models.
  4. Run A/B experiments, gather data, and perform statistical analysis.
  5. Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving.

Skills

Required

  • 3+ years of building models for business application experience
  • 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 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 professional software development
  • Experience in search advertising, search marketing, performance advertising, or similar digital advertising
  • Strong technical fluency in Generative AI
  • Deep understanding of large language models (LLMs), model fine tuning and prompt engineering
  • Ph.D. in computer science, mathematics, statistics, machine learning or equivalent quantitative field

What the JD emphasized

  • building models for business application experience
  • Generative AI
  • large language models (LLMs), model fine tuning and prompt engineering

Other signals

  • Generative AI
  • Machine Learning
  • LLMs
  • high scale low latency systems