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Job Details
Posted date: May 26, 2026
Location: Seattle, WA
Estimated salary: $257,350
Range: $218,800 - $295,900
Description
The Catalog Services Product Knowledge team is seeking a Sr. Applied Science Manager for leading initiatives for understanding, and scaling organization of product schema information. Our vision is simple: build AI systems that are capable of a deep product understanding, so we can organize and scale the catalog metadata (schema) for Amazon e-commerce catalog worldwide. This is a complex problem because the magnitude of products entities (attributes, values, constraints) to be modeled to cover all the Amazon products worldwide. You will lead a team of experienced Applied Scientists (direct reports) to create models and deliver them into the Amazon production ecosystem. Your efforts will build a robust ensemble of ML and GenAI techniques that will scale our catalog artifacts with a high precision across countries and languages.The leader will drive investments in machine learning, natural language processing, GenAI, to solve real world problems at scale. The team's output affects the velocity at which we build product schema and support the largest e-commerce catalog and impact million of customers. The team builds solutions ranging from automatic generation of product metadata, classification of entities, validation of concepts against customer traffic, creation of agents solving complex tasks mimicking human decisions at high precision, etc; all these developments drive true understanding of products at scale.
We are looking for an entrepreneurial, experienced Sr. Applied Science Manager who can turn a group of Machine Learning Scientists (PhD's in NLP, ML, GenAI) to produce best in class solutions. The ideal candidate has deep expertise in one or several of the following fields: Generative AI, Agents, LLMs, Web search, Applied/Theoretical Machine Learning, Deep Neural Networks, Classification Systems, Clustering, Natural Language Processing. S/he has a strong publication record at relevant academic venues and proven experience in launching products/features in the industry.
Key job responsibilities
In this team, you will:
- Manage business and technical requirements, design, be responsible for the overall coordination, quality, productivity and will be the primary point of contact for world-wide stakeholders of programs and goals that you lead.
- Partner with scientists, economists, and engineers to help deliver scalable ML scaled models, while building mechanisms to help our customers gain and apply insights, and build road maps for the projects you own.
- Track service levels and schedule adherence, and ensure the individual stakeholder teams meet and exceed their performance targets.
- Be expected to discover, define, and apply scientific, engineering, and business best practices.
- Manage and develop Applied Scientists (direct reports with a respective team).
About the team
The team's mission is to infer knowledge, understand, and derive product schema for all Amazon products entering the Catalog. The work is critical to power drive policies on how products will be merchandised, guide Selling Partners, inform models how to infer attributes. All this information drives the navigational Taxonomy, Search and Detail Page experiences, impacting million of customers. This is an already formed team with experience leading programs spanning services and ML initiatives. The leader collaborates closely with Software Managers, Sr. Leaders, and has exposure to multiple peer teams at Amazon who rely on this team's developments.
Qualifications
- 10+ years of building large-scale machine learning and AI solutions at Internet scale experience- Master's degree in Computer Science (Machine Learning, AI, Statistics, or equivalent)
- Experience managing and quantifying improvements in customer experience or value for the business resulting from research outcomes
- Experience hiring and leading experienced scientists as well as having a successful record of developing junior members from academia or industry to a successful career track
Extended Qualifications
- PhD in Computer Science (Machine Learning, AI, Statistics, or equivalent)- 10+ years of practical work applying ML to solve complex problems for large-scale applications experience
- 5+ years of hands-on work in big data, machine learning and predictive modeling experience
- 5+ years of people management experience
- Experience with big data technologies such as AWS, Hadoop, Spark, Pig, Hive etc.
- Experience in professional software engineering & best practices for the full software development life cycle, including coding standards, software architectures, code reviews, source control management, continuous deployments, testing, and operational excellence
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.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, WA, Seattle - 218,800.00 - 295,900.00 USD annually