Manager, Applied Science , Brand Protection ML

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

Manager for an Applied Science team focused on Brand Protection ML at Amazon. The role involves leading scientists to build and launch scalable AI/ML/LLM/GenAI solutions to identify and prevent infringement and counterfeit on Amazon's platform globally. The team works on complex business problems with significant customer impact, leveraging SOTA ML techniques and deep learning, computer vision, and NLP.

What you'd actually do

  1. You will lead a team of Applied Scientist to work backwards from customer needs and solve complex scientific problems that have a high business and customer impact.
  2. As a manager, You will be the thought leader for inventing novel science solutions using SOTA ML techniques including LLM and GenAI.
  3. You partner with your stakeholders and leadership to define the science vision and strategies for your team.
  4. You have excellent communication skills to explain complex scientific approaches to a variety of stakeholders and customers, and bridge the gap between science, tech, and business,.
  5. You are accountable for the science vision and strategic direction of your team, the artifacts they provide, and any technologies owned.

Skills

Required

  • PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field, or Master's degree and 4+ years of building machine learning models or developing algorithms for business application experience
  • 2+ years of scientists or machine learning engineers management experience
  • Knowledge of machine learning approaches and algorithms
  • Knowledge of ML, NLP, Information Retrieval and Analytics
  • 5+ years of building machine learning models or developing algorithms for business application experience

Nice to have

  • Experience building complex software systems, especially involving deep learning, machine learning and computer vision, that have been successfully delivered to customers
  • Experience with deep learning libraries such as PyTorch, TensorFlow, MxNet
  • Research publications in computer vision, deep learning or machine learning at peer-reviewed workshops, conferences or journals
  • Experience with the Scrum methodology (or similar alternatives) for agile software development

What the JD emphasized

  • building Generative AI solutions
  • identify and prevent infringement abuse and counterfeit
  • scalable AI models using machine learning, deep learning, LLM/Gen AI
  • launch scalable AI solutions operating on billions of Amazon product listings WW
  • inventing novel science solutions using SOTA ML techniques including LLM and GenAI
  • building complex software systems, especially involving deep learning, machine learning and computer vision, that have been successfully delivered to customers

Other signals

  • building Generative AI solutions
  • identify and prevent infringement abuse and counterfeit
  • scalable AI models using machine learning, deep learning, LLM/Gen AI
  • launch scalable AI solutions operating on billions of Amazon product listings WW