Principal Applied Scientist, AWS Marketplace & Partner Services

Overview

The AWS Marketplace & Partner Services (AMPS) Science team is at the forefront of driving AWS's AI-powered growth through developing and evaluating the next generation search, recommendations, and agentic systems. As a Principal Applied Scientist on our team, you'll own the technical strategy and execution for our most ambitious AI-driven initiatives that directly accelerate AWS revenue growth through both customer discovery experiences and partner co‑selling capabilities.

Your work will establish Marketplace as customers' primary solution discovery and transaction platform while simultaneously transforming how AWS partners scale their business through insights and agentic workflow automation.

• Define direction for next-generation recommendation systems, including deep personalization, agentic architecture, and primitives that integrate customer usage patterns, infrastructure requirements, and business objectives to deliver personalized, outcome-oriented guidance.
• Lead information retrieval innovations, multi‑objective ranking, and reasoning over heterogeneous solution types.
• Architect, implement, and design agentic AI systems that orchestrate complex workflows.
• Bridge theoretical innovations with practical solutions, making critical judgments to select the best technical approaches for both short and long‑term objectives.
• Mentor and guide applied scientists, holding the team to high standards of technical rigor and scientific excellence.
• Contribute to the broader scientific community through patents, publications at conferences, and engagement with academic partners.

• AWS Customers:
  Through the AWS Marketplace, we support discovery experiences that streamline cloud adoption and innovation.
• AWS Partners:
  Via Partner Central, we offer advanced tools and insights to enhance collaboration and drive mutual growth.
• Internal AWS Sellers:
  We equip our sales teams with data‑driven recommendations to better serve our customers and partners.

• 5+ years of practical work applying ML to solve complex problems.
• Experience working in predictive modeling and analysis.
• Experience programming in Java, C++, Python, or related language.
• Experience in machine learning, statistics, deep learning, natural language processing, or information retrieval.

• 10+ years of relevant work in industry or academia.
• Experience leading experienced scientists as well as developing junior members from academia or industry to a career track in a business environment.
• Experience creating novel algorithms and advancing the state of the art.
• Peer‑reviewed scientific contributions in premier journals and conferences.
• PhD in Electrical Engineering, Computer Science, Mathematics, or a related technical field.

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

Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies.

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, please visit (Use the "Apply for this Job" box below). for more information.

Base salary range for this position (US, CA, San Diego): $ - $ annually. Other locations:
NY, New York: $ - $; TX, Austin: $ - $; WA, Seattle: $ - $.

Benefits include health insurance (medical, dental, vision, prescription, Basic Life & ADDD insurance and optional 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 at .