Are you passionate about solving complex business problems at scale through Generative AI? Do you want to help build intelligent systems that reason, act, and learn from minimal supervision? 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 do this in close partnership with our selling partners, empowering them with best-in-class tools and expertise to offer a high-quality, compliant selection that customers trust. As an Applied Scientist I, you will bring subject matter expertise in at least one relevant discipline (e.g., NLP, computer vision, representation learning, agentic architecture) to contribute to next-generation agentic AI solutions that automate complex manual investigation processes at Amazon scale. Working alongside senior scientists, you will map business goals—such as reducing cost-of-serving while maintaining trust and safety standards—to well-defined scientific problems and metrics. You will invent, refine, and experiment with solutions spanning agentic reasoning, self-supervised representation learning, few-shot adaptation, multimodal understanding, and model compression. With guidance from senior scientists, you will stay current on research trends and benchmark your results against the state of the art. You will help design and execute experiments to identify optimal solutions, initiating the development and implementation of small components with team guidance. You will write secure, stable, testable, and well-documented production code at the level of an SDE I, rigorously evaluating models and quantifying performance. You will handle data in accordance with Amazon policies, troubleshoot issues to root cause, and ensure your work does not put the company at risk. Your scope of influence will typically be at the self-level, with the possibility of mentoring interns. You will participate in team design and prioritization discussions, learn the business context behind TSE's products, and escalate problems with proposed solutions. You will publish internal technical reports and may contribute to peer-reviewed publications and external review activities when aligned with business needs. This role offers a unique opportunity to contribute to end-to-end AI development—from research through production—with your contributions serving hundreds of millions of customers within months, not years. Key job responsibilities • Contribute to the design and development of agentic AI systems with multi-step reasoning, autonomous task execution, and multimodal intelligence, including feedback and memory mechanisms, leveraging reinforcement learning techniques for agent decision-making and policy optimization, with input and guidance from senior scientists • Help productionize models built on top of SFT (Supervised Fine-tuning) and RFT (Reinforced Fine-tuning) approaches, as well as few-shot approaches based on multimodal datasets spanning text, images, and structured data, applying mathematical optimization techniques to improve efficiency, resource allocation, and decision-making in complex workflows, working alongside senior scientists to identify optimal solutions • Contribute to building production-ready deep learning and conventional ML solutions, including multimodal fusion and cross-modal alignment techniques that seamlessly connect visual, textual, and relational understanding, to support automation requirements within your team's scope • Help identify customer and business problems; use reasonable assumptions, data, and customer requirements to solve well-defined scientific problems involving multimodal inputs such as unstructured text, documents, product images, and relational data, developing representations that capture complementary signals across modalities and mapping business goals to scientific metrics • May co-author research papers for peer-reviewed internal and/or external venues, including contributions in areas such as multimodal representation learning and vision-language modeling, and contribute to the wider scientific community by reviewing research submissions, when aligned with business needs • Prototype rapidly, iterate based on feedback, and deliver small components at SDE I level—including multimodal data pipelines and inference modules—that integrate into production-scale systems • Write secure, stable, testable, maintainable, and well-documented code, balancing model capability, deployment cost, and resource usage across multimodal architectures while understanding state-of-the-art data structures, algorithms, and performance tradeoffs • Rigorously test code and evaluate models across individual and combined modalities, quantifying their performance; troubleshoot issues, research root causes, and thoroughly resolve defects, leaving systems more maintainable • Participate in team design, scoping, and prioritization discussions through clear verbal and written communication; seek to learn the business context, science, and engineering behind your team's products, including how multimodal signals contribute to trust and safety decisions • Participate in engineering best practices with peer reviews; clearly document approaches and communicate design decisions; publish internal technical reports to institutionalize scientific learning • Help train and mentor scientist interns; identify and escalate problems with proposed solutions, taking ownership or ensuring clear hand-off to the right owner About the team Trustworthy Shopping Experience Product team in TSE is responsible for the human-in-the-loop products and technology used in the risk investigations at Amazon. The 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.
May 2, 2019Traditionally, Alexa has interpreted customer requests according to their intents and slots. If you say, “Alexa, play ‘What’s Going On?’ by Marvin Gaye,” the intent should be PlayMusic, and “‘What’s Going On?’” and “Marvin Gaye” should fill the slots SongName and ArtistName.
