Applied Scientist II, Shopping ; TSE

Position: Applied Scientist II, Trustworthy Shopping Experience (TSE)

Applied Scientist II, Trustworthy Shopping Experience (TSE)

Are you passionate about solving complex business problems at scale through Generative AI? Do you want to build intelligent systems that reason, act, and learn from minimal supervision? Are you excited about taking innovative AI solutions from proof-of-concept to production? If so, we have an exciting opportunity for you on Amazon's Trustworthy Shopping Experience (TSE) team.

At TSE, our vision is to guarantee customers a worry-free shopping experience by earning their trust that the products they buy are safe, authentic, and compliant with regulations and policy. We give customers confidence that Amazon stands behind every product and will make it right in the rare chance anything goes wrong. We do this in close partnership with our selling partners and empower them with best-in-class tools and expertise required to offer a high-quality selection of compliant products that customers trust.

As an Applied Scientist, you will lead the development of next Gen AI solutions to automate complex manual investigation processes at Amazon scale. You will work on some of the most fascinating challenges in applied AI—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, and compress complex model capabilities into efficient, deployable systems.

Your innovations will deliver significant impact to cost-of-serving customers while maintaining the highest standards of trust and safety.

This role offers end-to-end ownership—from initial research and proof-of-concept through production deployment. You will see your innovations serving hundreds of millions of customers within months, not years.

Responsibilities

• 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.
• Productionize large scale models built on top of SFT (Supervised Fine tuning) and RFT (Reinforced fine tuning) approaches, few shot approaches based on multimodal datasets
• Build novel production ready Deep and conventional ML solutions to aid the multiple potential automation requirements
• 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
• Author or co-author research papers for peer-reviewed venues; serve as PC member at conferences when aligned with business needs
• Prototype rapidly, iterate based on feedback, and deliver components at SDE I+ level that integrate directly into production-scale systems
• Engineer efficient systems balancing model capability, deployment cost, and resource usage; write significant code demonstrating technical excellence and maintainability
• Scrutinize algorithm and software performance for improvements; resolve root causes leaving systems more maintainable
• Contribute to tactical and strategic planning—team goals, priorities, and roadmaps—while providing architectural guidance for AI systems
• Participate in engineering best practices with rigorous peer reviews; communicate design decisions clearly and participate in science reviews
• Train new teammates on component construction and integration; mentor less experienced scientists and participate in hiring processes

About the team

Investigation technology Product team in TSE is responsible for the human-in-the-loop products and technology used in the risk investigations team is also responsible for reducing the cost of performing the investigations, by automating wherever possible and optimizing the experience where manual interventions are needed. The team leverages state-of-the art technology and GenAI to deliver the products and associated goals.

Basic Qualifications

• 3+ years of building models for business application experience
• PhD, or Master's degree and 3+ years of CS, CE, ML or related field experience
• Experience in patents or publications at…