Applied Scientist Ii, Trustworthy Shopping Experience (tse)

Amazon Amazon · Big Tech · IN, KA, Bengaluru · Applied Science

Applied Scientist II role focused on building and deploying Generative AI solutions for Amazon's Trustworthy Shopping Experience (TSE) team. The role involves creating intelligent systems with multi-step reasoning, autonomous task execution, and multimodal intelligence, leveraging techniques like SFT and RFT. It requires end-to-end ownership from research to production, impacting millions of customers.

What you'd actually do

  1. Design and build expertise agentic AI systems with multi-step reasoning, autonomous task execution, and multimodal intelligence with capabilities to handle feedback with memory mechanisms.
  2. Productionize large scale models built on top of SFT (Supervised Finetuning) and RFT (Reinforced fine tuning) approaches, few shot approaches based on multimodal datasets
  3. Build novel production ready Deep and conventional ML solutions to aid the multiple potential automation requirements
  4. Identify customer and business problems at project level; invent or extend state-of-the-art approaches for complex workflows involving unstructured text, documents, images, and relational data
  5. Author or co-author research papers for peer-reviewed venues; serve as PC member at conferences when aligned with business needs

Skills

Required

  • building models for business application
  • PhD or Master's degree and 3+ years of CS, CE, ML or related field experience
  • experience in patents or publications at top-tier peer-reviewed conferences or journals
  • 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

  • Unix/Linux experience
  • professional software development experience

What the JD emphasized

  • building systems that reason and act autonomously
  • learn rich representations from structured and relational data without extensive labels
  • adapt rapidly from limited examples
  • improve through feedback and interaction
  • seamlessly connect visual and textual understanding
  • compress complex model capabilities into efficient, deployable systems
  • end-to-end ownership
  • proof-of-concept through production deployment
  • serving hundreds of millions of customers

Other signals

  • end-to-end ownership
  • production deployment
  • hundreds of millions of customers