Senior Applied Scientist, Off-Search, Sponsored Products and Brands

The Sponsored Products and Brands (SPB) team at Amazon Ads is transforming advertising through generative AI technologies. We help millions of customers discover products and engage with brands across and beyond. Our team combines human creativity with artificial intelligence to reinvent the entire advertising lifecyclefrom ad creation and optimization to performance analysis and customer insights.

We develop responsible AI technologies that balance advertiser needs enhance shopping experiences and strengthen the marketplace. Our team values innovation and tackles complex challenges that push the boundaries of whats possible with AI.

Join us in shaping the future of advertising.

Key job responsibilities
This role will redesign how ads create personalized relevant shopping experiences with customer value at the forefront. Key responsibilities include:

- 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 teams 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.

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. This is a rare opportunity to redefine how ads are retrieved allocated and/or experiencedelevating 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 Amazons rich data and the worlds 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.

About the team
The Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazonsuch as product detail pages the homepage and store-in-store pagesto 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 stackfrom backend ads-retail edge services ads retrieval and ad auctions to shopper-facing experiencesall designed to deliver meaningful value.

- PhD or Masters 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
- Strong foundation in GenAI large language models machine learning deep learning probabilistic modeling and/or optimization.

- 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

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status disability or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process including support for the interview or onboarding process please visit for more information. If the country/region youre applying in isnt listed please contact your Recruiting Partner.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $150400/year in our lowest geographic market up to $260000/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge skills and experience. Amazon is a total compensation company. Dependent on the position offered equity sign-on payments and other forms of compensation may be provided as part of a total compensation package in addition to a full range of medical financial and/or other benefits. For more information please visit This position will remain posted until filled. Applicants should apply via our internal or external career site.