GenAI Scientist: Multimodal ML for Billion-Product Catalog

Amazon Science; Seattle (WA); On-site; USD 143,000 - 193,000; Full time

Job summary

Amazon Science is seeking a research-focused professional to advance GenAI and multimodal learning for the Amazon Selection and Catalog Systems. You will tackle research problems and translate business challenges into rigorous scientific frameworks.

The role involves designing models for billion-product-scale catalogs, developing end-to-end ML pipelines, and contributing to publishable work. A PhD or Master’s with substantial experience is expected, with production-ready AI capabilities.

Qualifications

• PhD or Master's with 4+ years in CS/CE/ML or related field.
• Experience programming in Java, C++, Python.
• 2+ years of building machine learning models or developing algorithms for business applications.
• Experience with LLMs or large-scale deep learning systems.

Responsibilities

• Formulate novel research problems at the intersection of GenAI, multimodal learning, and large-scale information retrieval.
• Design and implement leading models leveraging frontier models and agentic architectures for rich catalog information at billion-product scale.
• Pioneer explainable AI methodologies balancing performance with production scalability for systems affecting millions of daily decisions.
• Own end-to-end ML pipelines from research ideation to production deployment, processing petabytes of multimodal data with rigorous evaluation frameworks.
• Represent the team in the science community by publishing findings and delivering tech talks.

Skills

Java

C++

Python

LLMs

Large-scale DL

Machine learning

Education

CS/CE/ML related degree

Job description

Amazon Science is seeking a research-focused professional to advance GenAI and multimodal learning for the Amazon Selection and Catalog Systems. You will tackle research problems and translate business challenges into rigorous scientific frameworks.

The role involves designing models for billion-product-scale catalogs, developing end-to-end ML pipelines, and contributing to publishable work. A PhD or Master’s with substantial experience is expected, with production-ready AI capabilities.