Machine Learning Scientist - Genai, Kit

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

Machine Learning Scientist role focused on Generative AI within AWS, aiming to identify customer needs and improve cloud adoption. The role involves building Agentic AI systems, fine-tuning LLMs, applying Reinforcement Learning, and generating insights from large datasets, with a focus on taking ideas from conception to production.

What you'd actually do

  1. Drive innovation with Gen AI to bring paradigm shift to how the business operates and build “best in the world” experience that customers will love!
  2. Lead, invent, and design tech that will directly impact every customer across all AWS services.
  3. Be a key driver in taking something from an idea to an experiment to a prototype and finally to a live production system.
  4. Lead and establish a culture for the big things to come.

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 in patents or publications at top-tier peer-reviewed conferences or journals
  • 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

  • PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
  • Experience in professional software development

What the JD emphasized

  • building Agentic AI systems
  • fine-tuning Large language models for domain specific use cases
  • Reinforcement Learning
  • generating strategic insights and recommendations from very large datasets
  • building models for business application experience
  • patents or publications at top-tier peer-reviewed conferences or journals

Other signals

  • building Agentic AI systems
  • fine-tuning Large language models for domain specific use cases
  • Reinforcement Learning
  • generating strategic insights and recommendations from very large datasets
  • taking something from an idea to an experiment to a prototype and finally to a live production system