Senior Applied Scientist , Alexa AI Aurora

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

Senior Applied Scientist role focused on advancing conversational AI technologies, specifically LLMs and generative AI, for Alexa. The role involves defining science roadmaps, architecting agentic systems, establishing evaluation frameworks, and driving end-to-end delivery of research initiatives from experimentation to production. Emphasis on building scalable agentic systems for conversation understanding and generation, and contributing to the team's scientific reputation through publications and patents.

What you'd actually do

  1. Define the science roadmap and advance core science primitives for conversation modelling, content generation, and automated quality assurance via state-of-the-art computer vision, machine learning, and generative AI
  2. Architect agentic systems, making high-judgment trade-offs across audio/text/visual quality, relevance, latency, cost, and long-term extensibility
  3. Establish evaluation frameworks, metrics, and success criteria for the team's scientific initiatives, institutionalizing rigorous validation across customer touch points
  4. Own end-to-end delivery of complex, ambiguous research initiatives from problem formulation through experimentation to production deployment, with minimal guidance
  5. Identify whitespace opportunities by staying at the forefront of AI/ML advances and translating them into actionable research directions with clear customer and business impact

Skills

Required

  • building machine learning models for business application
  • PhD, or Master's degree and 6+ years of applied research experience
  • programming in Java, C++, Python or related language
  • neural deep learning methods
  • machine learning

Nice to have

  • building machine learning models for business application
  • large scale distributed systems such as Hadoop, Spark etc.
  • building speech recognition, machine translation and natural language processing systems (e.g., commercial speech products or government speech projects)
  • Generative AI tools to enhance workflow efficiency
  • agentic prompting
  • evaluation practices

What the JD emphasized

  • state-of-the-art
  • scalable solutions
  • highly iterative environment
  • absolute requirements
  • customer impact
  • production deployment
  • customer touch points

Other signals

  • LLM
  • conversational AI
  • generative AI
  • agentic systems