Applied Scientist II, AERO Agentic AI Team

Description

Amazon is seeking passionate, talented and inventive Applied Scientists with a strong machine learning background to help build Agentic AI solutions for Selling Partners and Retail users. Our mission is to elevate their experience through intelligent, collaborative Agentic AI solution powered by specialized agents that deliver seamless, personalized support at scale.

Key job responsibilities

As part of our AERO team in Amazon PGX, you will work alongside internationally recognized experts to develop novel solutions and modeling techniques to advance the state-of-the-art in Generative and Agentic AI. Your work will directly impact millions of our users in the form of products and services that improves their experience. You will gain hands on experience with Amazon's native / partnered Large Language Models (LLMs), technologies like AgentCore, AgentMemory, Strands and Model Context Protocol (MCPs). We are also looking for talents with experiences / expertise in building large-scale and high-performing systems.

Basic Qualifications

• 3+ years of building models for business application experience
• PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
• Experience in patents or publications at top-tier peer-reviewed conferences or journals
• Experience programming in Java, C++, Python or related language
• Experience in any of the following areas : algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

Preferred Qualifications

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 for more information. If the country / region you're applying in isn't listed, please contact your Recruiting Partner.

Skills Required

Data Mining, Unix, Java, Python, Parsing, Linux, Machine Learning