The Ads Marketing Decision Science team builds intelligent, data-driven systems that transform advertiser experiences through precise personalization and automated optimization. We decode complex patterns in advertiser behavior, content effectiveness, and performance signals to power real-time, contextual marketing decisions at scale — moving Amazon Ads from rules-based relevancy to true AI-driven personalization. Our work spans four pillars: Advertiser DNA (behavioral fingerprinting to predict advertiser needs and growth opportunities), Content Intelligence (frameworks to evaluate, select, and generate marketing content aligned to advertiser context), Automated Decision Systems (ML-powered audience targeting and next-best-action recommendations), and Gen-AI Applications (contextual, natural interactions across marketing touchpoints). As a Senior Applied Scientist on the team, you will be at the forefront of our Gen-AI applications, leading the science behind conversational and agentic experiences that help advertisers grow. This role demands a strong foundation in machine learning and in LLM/NLP — deep fundamentals that you apply to build robust, production-grade systems rather than treating models as black boxes. In particular, you will own the development of our chatbot capability — designing the agentic reasoning, retrieval, and evaluation systems that make these interactions accurate, helpful, and trustworthy. You will set the technical vision, innovate on behalf of our customers, and take solutions end-to-end from inception to production. You will partner closely with engineering to deploy at scale and low latency, and with product and business teams to ensure the experience meets real advertiser needs. Key job responsibilities • Lead the design and development of the chatbot/agentic AI capability for WeChat and other third-party channels, from concept through production. • Bring strong ML and LLM/NLP fundamentals to bear on system design — grounding architecture and modeling choices in a deep understanding of the underlying methods. • Architect and build agentic AI systems — planning, tool use, and multi-step reasoning — grounded in Retrieval-Augmented Generation (RAG) over Amazon Ads knowledge sources. • Apply reinforcement learning and model fine-tuning (e.g., instruction tuning, RLHF/RLAIF, preference optimization) to adapt large language models to our domain and channels. • Define and operationalize rigorous LLM evaluation: golden sets, faithfulness/groundedness, precision/recall, and human-in-the-loop evaluation mechanisms that reliably measure and improve quality. • Own applied engineering quality of the science stack — PyTorch modeling, well-designed APIs, and latency/cost optimization for real-time, production-grade interactions. • Collaborate with engineering, product management, and business teams to define requirements and ship measurable customer impact. • Drive continuous improvement through experimentation, iterative development, testing, and optimization. • Translate complex scientific challenges into clear, impactful solutions for business stakeholders. • Mentor and guide junior scientists, fostering a collaborative, high-performing team culture, and engage the broader scientific community through presentations, publications, and patents. About the team We are a team of Applied Scientists, Research Scientists, Data Scientists, and Business Intelligence Engineers with deep expertise in ML, NLP, Gen-AI, RL, and causal inference, from a diverse range of backgrounds. We partner closely with strong engineers, product managers, and sales leaders who bring ads-industry depth and experience building scalable modeling and software solutions.
