Amazon
Sr. Applied Scientist, Sponsored Products and Brands Off-Search
Amazon, Seattle, Washington, us, 98127
Sr. Applied Scientist, Sponsored Products and Brands Off-Search
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. 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. Key job responsibilities include: 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. Contribute to the enhancement of 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. A day in the life: 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. Basic Qualifications
Basic Qualifications: PhD, or Master's degree and 8+ 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 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. Preferred Qualifications
Preferred Qualifications: 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. Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
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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. 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. Key job responsibilities include: 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. Contribute to the enhancement of 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. A day in the life: 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. Basic Qualifications
Basic Qualifications: PhD, or Master's degree and 8+ 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 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. Preferred Qualifications
Preferred Qualifications: 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. Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
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