Amazon Principal Applied Scientist - GenAI Amazon Sponsored Products 

United States, New York, New York 

123074559

Key job responsibilities
This role will be pivotal in rethinking how ads contribute to a personalized, relevant, and inspirational shopping experience, with the customer value proposition at the forefront. Key responsibilities include, but are not limited to:* Lead the design and development of GenAI-powered solutions to transform ad retrieval, auctions, whole-page relevance, and bespoke shopping experiences.
* Collaborate cross-functionally with scientists, engineers, and product leaders to bring scalable, production-ready GenAI solutions to life.
* Stay abreast of industry trends in GenAI, LLMs, and related disciplines, bringing fresh and innovative concepts, ideas, and prototypes to the organization.
* Champion a long-term scientific vision for advertising that aligns with customer value, business strategy, and the technological frontier.
* Play a critical role in enhancing the team’s scientific and technical rigor by identifying and implementing best-in-class algorithms, methodologies, and infrastructure that enable rapid experimentation and scaling.
* Mentor and grow junior scientists and engineers, cultivating a high-performing, collaborative, and intellectually curious team.

* Ph.D. in Computer Science, Machine Learning, Natural Language Processing, Statistics, or a related quantitative discipline.
* Experience in building scalable AI/ML solutions for 5+ years, including at least 3 years leading large, complex projects at scale.
* Strong foundation in machine learning, deep learning, probabilistic modeling, and/or optimization.
* Experience developing and deploying models in real-world production environments.

* Proven expertise in Generative AI, foundation models, LLMs, and/or fine-tuning and customization for downstream tasks.
* Hands-on experience in ads ranking, retrieval, recommendation systems, search, or personalization at web scale.
* Deep understanding of multi-modal modeling, few-shot learning, retrieval-augmented generation (RAG), or reinforcement learning from human feedback (RLHF).
* Experience with online experimentation, A/B testing frameworks, and metrics design for advertising or e-commerce.
* Demonstrated ability to communicate complex technical topics clearly to both technical and non-technical audiences.
* Experience in computational advertising, including familiarity with auction theory, ad economics, and advertiser performance metrics.