Applied Scientist, Alexa Smart Properties

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

Applied Scientist role focused on building LLM-driven conversational assistants for enterprise use cases in hospitality and senior living, leveraging Amazon's scale and data. Responsibilities include developing core LLM technologies, prompt optimization, and building/measuring metrics for these systems.

What you'd actually do

  1. You will analyze, understand and improve user experiences by leveraging Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in artificial intelligence.
  2. You will work on core LLM technologies, including developing best-in-class modeling, prompt optimization algorithms to enable Conversation AI use cases.
  3. Build and measure novel online & offline metrics for personal digital assistants and customer scenarios, on diverse devices and endpoints.
  4. Create, innovate, and deliver deep learning, policy-based learning, and/or machine learning-based algorithms to deliver customer-impacting results.
  5. Perform model/data analysis and monitor metrics through online A/B testing.

Skills

Required

  • 3+ years of building models for business application experience
  • PhD, or Master's degree and 4+ years of designing experiments and statistical analysis of results experience
  • Experience programming in Java, C++, Python or related language
  • Knowledge of standard speech and machine learning techniques

Nice to have

  • Experience in designing experiments and statistical analysis of results
  • Have publications on top-tier conferences, such as CVPR, ICCV, ECCV or NeurIPS
  • Experience applying theoretical models in an applied environment

What the JD emphasized

  • enterprise-grade
  • highly available
  • large scale solution
  • core LLM technologies
  • prompt optimization algorithms
  • Conversation AI use cases
  • Amazon-scale use cases
  • online A/B testing

Other signals

  • LLM-based enterprise-grade solution
  • Amazon-scale use cases
  • conversational assistants
  • prompt optimization