Applied Scientist, Off-Search Relevance, Products

Applied Scientist, Off-Search Relevance, Sponsored Products

Job  |  Services LLC

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 on and beyond  We are at the forefront of reinventing 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.

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.

The Off‑Search team within SPB focuses on building delightful ad experiences across various surfaces beyond Search—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.

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, operating full stack from backend ads‑retail edge services, ads retrieval, and ad auctions to shopper‑facing experiences—all designed to deliver meaningful value.

• Contribute to the design and development of GenAI, deep learning, multi‑objective optimization, and/or reinforcement learning‑empowered solutions to transform ad retrieval, auctions, whole‑page relevance, and/or bespoke shopping experiences.
• Collaborate cross‑functionally with other scientists, engineers, and product managers to bring scalable, production‑ready science 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.
• Enhance 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.

As an Applied Scientist on the Off‑Search team, you will contribute to the development of Generative AI (GenAI) and Large Language Models (LLMs) to revolutionize our advertising flow, backend optimization, and frontend shopping experiences. You’ll redefine how ads are retrieved, allocated, and experienced—elevating them into personalized, contextually aware, and inspiring components of the customer journey. You’ll transform areas such as ad retrieval, ad allocation, whole‑page relevance, and differentiated recommendations through GenAI.

By building novel generative models grounded in Amazon’s rich data and world 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.

• PhD, or Master’s degree and 4+ years of CS, CE, ML or related field experience
• 3+ years of building models for business application experience
• Experience programming in Java, C++, Python or related language
• Strong foundation in GenAI, large language models, 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,…