About the position

The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining the advertising landscape through state-of-the-art generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. Curious about our advertising solutions? Discover more about Sponsored Products and Sponsored Brands to see how we’re helping businesses grow on Amazon.com and beyond! As an Applied Scientist on the Sponsored Products and Brands Off-Search team, you will contribute to the development in Generative AI (GenAI) and Large Language Models (LLMs) to revolutionize our advertising flow, backend optimization, and frontend shopping experiences. This is a rare opportunity to redefine how ads are retrieved, allocated, and/or experienced—elevating them into personalized, contextually aware, and inspiring components of the customer journey. You will have the opportunity to fundamentally transform areas such as ad retrieval, ad allocation, whole-page relevance, and differentiated recommendations through the lens of GenAI. By building novel generative models grounded in both Amazon’s rich data and the world’s collective knowledge, your work will shape how customers engage with ads, discover products, and make purchasing decisions. If you are passionate about applying frontier AI to real-world problems with massive scale and impact, this is your opportunity to define the next chapter of advertising science. The Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazon—such as product detail pages, the homepage, and store-in-store pages—to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, remains relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Amazon Stores stakeholders to lead the expansion and growth of advertising across Amazon-owned and -operated pages beyond Search. We operate full stack—from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences—all designed to deliver meaningful value.

Responsibilities

• Design and develop solutions using GenAI, deep learning, multi-objective optimization and/or reinforcement learning to transform ad retrieval, auctions, whole-page relevance, and shopping experiences.
• Partner with scientists, engineers, and product managers to build scalable, production-ready science solutions.
• Apply industry advances in GenAI, Large Language Models (LLMs), and related fields to create innovative prototypes and concepts.
• Improve the team's scientific and technical capabilities by implementing algorithms, methodologies, and infrastructure that enable rapid experimentation and scaling.
• Mentor junior scientists and engineers to build a high-performing, collaborative team.

Requirements

• PhD, or Master's degree and 6+ years of applied research experience
• 3+ years of building machine learning models for business application experience
• Experience programming in Java, C++, Python or related language
• Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience in machine learning, data mining, information retrieval, statistics or natural language processing

Nice-to-haves

• Demonstrated expertise in Generative AI technologies, including foundation models, LLMs, and model customization for specific business applications
• Hands-on experience building ads ranking, retrieval, recommendation, or personalization systems that operate at web scale
• Technical proficiency in advanced AI approaches such as multi-modal modeling, few-shot learning, retrieval-augmented generation (RAG), or reinforcement learning from human feedback (RLHF)
• Track record of designing and implementing online experimentation frameworks, including A/B testing methodologies and performance metrics for advertising or e-commerce
• Proven ability to translate complex technical concepts into clear explanations for diverse audiences, from engineers to executives
• Deep knowledge of computational advertising fundamentals, including auction mechanisms, advertising economics, and advertiser success metrics

Benefits

• health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage)
• 401(k) matching
• paid time off
• parental leave