Description 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. Basic Qualifications - 6+ years of building machine learning models for business application experience - PhD, or Master's degree and 6+ years of applied research experience - Experience programming in Java, C++, Python or related language - Experience applying machine learning to solve real-world problems in large-scale systems - Strong understanding of digital advertising, search, or e-commerce ecosystems - Familiarity with statistical hypothesis testing, A/B testing, and causal inference for robust experimental evaluation - Ability to translate ambiguous business goals (e.g., improving ad relevance, increasing advertiser value) into well-defined model objectives and metrics Preferred Qualifications - Experience with large-scale retrieval, relevance modeling, or search ranking - Familiarity with LLMs or GenAI, and interest in applying them to improve ad sourcing and shopper relevance - Knowledge of multi-objective optimization (e.g., gradient descent, Pareto optimization) - Strong background in machine learning, including deep learning and representation learning - Experience with search technologies such as Lucene, Solr, or ElasticSearch, and interest in innovating at the intersection of classical IR and modern ML retrieval - Demonstrated ability to lead ambiguous problem spaces and validate solutions through experimentation - Research publications or open-source contributions in ML, IR, or NLP are a plus 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 https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t 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 $150,400/year in our lowest geographic market up to $260,000/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 https://www.aboutamazon.com/workplace/employee-benefits . This position will remain posted until filled. Applicants should apply via our internal or external career site.