Sponsored Brands (SB) is a multi-billion dollar business within Amazon Ads that helps brands grow by connecting them with shoppers through engaging, creative ad formats across search and detail pages. SB plays a key role in driving brand discovery and long-term customer relationships on Amazon.
The SB Ad Sourcing and Relevance team is responsible for selecting high-quality, contextually relevant ads from a vast candidate pool. We combine semantic retrieval, real-time personalization, and deep learning-based relevance modeling to surface the most relevant ads for each shopper and query. Our work directly impacts shopper engagement, advertiser success, and marketplace efficiency.
We are looking for a Sr. Applied Scientist to lead innovation at the intersection of large-scale retrieval, ML, and shopper relevance. You will develop and deploy models that improve ad coverage and quality, experiment at scale, and collaborate with cross-functional teams to deliver measurable business impact. This is a high-ownership role with significant visibility and opportunity to shape the future of brand advertising on Amazon.
Key job responsibilities
* Design and implement machine learning models for SB ad retrieval, semantic search, and contextual relevance to improve ad coverage, engagement, and match quality.
* Partner with product and science teams to define and evolve shopper relevance for Sponsored Brands—a complex and multi-faceted concept involving brand identity, product context, creative type, and shopper intent.
* Build scalable methods to measure, model, and predict relevance across different ad formats and placements.
* Investigate how to best apply relevance predictions in ad selection and auction ranking to improve long-term shopper engagement and advertiser value.
* Develop multi-objective optimization techniques to balance competing goals such as relevance, CTR, brand discoverability, and marketplace fairness.
* Lead large-scale A/B testing and offline evaluations to validate model impact and guide roadmap decisions.
* Work closely with engineers to productionize models and ensure performance in real-time serving environments.
* Stay current with advances in information retrieval, representation learning, and relevance modeling, and translate research into applied innovations.
* Mentor junior scientists and contribute to hiring and growing a high-performing science team.
* Communicate technical findings and strategic insights clearly to stakeholders across science, engineering, and product.
About the team
The SB Ad Sourcing and Relevance team builds the core systems that retrieve, select, and rank Sponsored Brands ads across Amazon. We develop large-scale, low-latency infrastructure and machine learning models to connect shoppers with the most relevant brand experiences. Our work spans semantic retrieval, deep relevance modeling, personalization, and multi-objective optimization. We operate at the intersection of modern research and high-impact applications, partnering closely with teams across Amazon Ads to improve shopper engagement, advertiser outcomes, and marketplace efficiency.
