Principal Applied Scientist, Console Science

Amazon Amazon · Big Tech · Santa Clara, CA · Applied Science

Principal Applied Scientist role focused on building industry-leading Conversational AI Systems using Generative AI, LLMs, NLU, Dialog Systems, and Applied ML. The role involves developing novel algorithms and modeling techniques to advance human language technology, impacting millions of customers through products and services. It requires a PhD, extensive experience in ML model building for business applications, and a strong publication/patent record. The team uses generative AI and foundation models to reimagine customer experiences on AWS.

What you'd actually do

  1. Develop novel algorithms and modeling techniques to advance the state-of-the-art in human language technology.
  2. Build industry-leading Conversational AI Systems.
  3. Work with Amazon’s heterogeneous text, structured data sources, and large-scale computing resources to accelerate advances in language understanding.
  4. Explore new technologies and find creative solutions.
  5. Impact the life of engineers around the world.

Skills

Required

  • PhD and 8+ years of CS, CE, ML or related field experience
  • 8+ years of building models for business application experience
  • Experience in patents or publications at top-tier peer-reviewed conferences or journals in NLP and Applied Machine Learning.
  • 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 and 8+ years of CS, CE, ML or related field experience
  • 8+ years of building models for business application experience
  • Experience in patents or publications at top-tier peer-reviewed conferences or journals in NLP and Applied Machine Learning.
  • 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

What the JD emphasized

  • patents or publications at top-tier peer-reviewed conferences or journals in NLP and Applied Machine Learning
  • building models for business application experience

Other signals

  • Generative AI with Large Language Models (LLMs)
  • Conversational AI Systems
  • NLU
  • Dialog Systems
  • Applied Machine Learning (ML)