Applied Scientist, Spx AI Lab

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

Applied Scientist role focused on building and deploying production-grade, multi-agent generative AI systems for Amazon's Seller Assistant, impacting millions of sellers worldwide. The role involves creating next-generation tools, designing and deploying innovative models, and establishing scalable processes for model implementation and validation.

What you'd actually do

  1. Use state-of-the-art Machine Learning and Generative AI techniques to create the next generation of the tools that empower Amazon's Selling Partners to succeed.
  2. Design, develop and deploy highly innovative models to interact with Sellers and delight them with solutions.
  3. Work closely with teams of scientists and software engineers to drive real-time model implementations and deliver novel and highly impactful features.
  4. Establish scalable, efficient, automated processes for large scale data analyses, model benchmarking, model validation and model implementation.
  5. Research and implement novel machine learning and statistical approaches.

Skills

Required

  • building machine learning models
  • developing algorithms for business application
  • programming in Java, C++, Python or related language
  • algorithms and data structures
  • parsing
  • numerical optimization
  • data mining
  • parallel and distributed computing
  • high-performance computing

Nice to have

  • patents or publications at top-tier peer-reviewed conferences or journals
  • investigating, designing, prototyping, and delivering new and innovative system solutions
  • leveraging generative AI tools to enhance workflow efficiency and productivity
  • craft effective prompts and critically evaluate AI-generated outputs
  • identifying opportunities to integrate AI solutions into products and services

What the JD emphasized

  • production-grade
  • multi-agent system
  • Amazon's scale
  • millions of sellers

Other signals

  • multi-agent system
  • production-grade
  • Amazon's scale
  • customer insight to shipped product