Applied Scientist, Seller Fees Science & Tech

The Seller Fees organization drives the monetization infrastructure powering Amazon's global marketplace, processing billions of transactions for over two million active third-party sellers worldwide. Our team owns the complete technical stack and strategic vision for fee computation systems, leveraging advanced machine learning to optimize seller experiences and maintain fee integrity at unprecedented scale.

We're seeking an exceptional Applied Scientist to push the boundaries of large-scale ML systems in a business-critical domain. This role presents unique opportunities to
• Architect and deploy state-of-the-art transformer-based models for fee classification and anomaly detection across hundreds of millions of products • Pioneer novel applications of multimodal LLMs to analyze product attributes, images, and seller metadata for intelligent fee determination • Build production-scale generative AI systems for fee integrity and seller communications • Advance the field of ML through novel research in high-stakes, large-scale transaction processing • Develop SOTA causal inference frameworks integrated with deep learning to understand fee impacts and optimize seller outcomes • Collaborate with world-class scientists and engineers to solve complex problems at the intersection of deep learning, economics, and large business systems.

If you're passionate about advancing the state-of-the-art in applied ML/AI while tackling challenging problems at global scale, we want you on our team!

Key job responsibilities
Responsibilities:
. Design measurable and scalable science solutions that can be adopted across stores worldwide with different languages, policy and requirements.
· Integrate AI (both generative and symbolic) into compound agentic workflows to transform complex business systems into intelligent ones for both internal and external customers.
· Develop large scale classification and prediction models using the rich features of text, image and customer interactions and state-of-the-art techniques.
· Research and implement novel machine learning, statistical and econometrics approaches.
· Write high quality code and implement scalable models within the production systems.
· Stay up to date with relevant scientific publications.
· Collaborate with business and software teams both within and outside of the fees organization.

Basic Qualifications

- Experience programming in Java, C++, Python or related language
- Experience with SQL and an RDBMS (e.g., Oracle) or Data Warehouse

Preferred Qualifications

- Experience implementing algorithms using both toolkits and self-developed code
- Have publications at top-tier peer-reviewed conferences or journals

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 $129,400/year in our lowest geographic market up to $212,800/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.