GenAI Catalog Scientist: Multimodal & Large-Scale

Amazon Science; New York (NY); On-site; USD 172,000 - 223,000; Full time

Job summary

Amazon Science in New York seeks an applied scientist to advance GenAI and multimodal catalog understanding at scale. You will partner with teams to build state-of-the-art algorithms and services that infer product-to-product relationships across billions of products.

This role emphasizes production deployment, explainable AI, and mentoring researchers while publishing findings and driving innovation in agentic shopping experiences.

Qualifications

• PhD or Master's with 4+ years in CS/CE/ML.
• Experience programming in Java, C++, Python.
• Publications in top-tier conferences or journals.

Responsibilities

• Formulate novel research problems at the intersection of GenAI, multimodal learning, and large-scale information retrieval—translating ambiguous business challenges into tractable scientific frameworks
• Design and implement models leveraging VLMs, foundation models, and agentic architectures for product identity and catalog understanding at billion-product scale
• Pioneer explainable AI methodologies balancing performance with production scalability
• Own end-to-end ML pipelines from research ideation to production deployment using petabytes of data
• Define research roadmaps aligned with business priorities and product improvements
• Mentor peer scientists and engineers on advanced ML techniques and experimental design
• Represent the team in the science community with publications and talks

Skills

GenAI concepts

Multimodal learning

Large-scale information retrieval

Java

C++

Python

Education

PhD

Master's degree

Tools

VLMs

Foundation models

Agentic architectures

Job description

Amazon Science in New York seeks an applied scientist to advance GenAI and multimodal catalog understanding at scale. You will partner with teams to build state-of-the-art algorithms and services that infer product-to-product relationships across billions of products.

This role emphasizes production deployment, explainable AI, and mentoring researchers while publishing findings and driving innovation in agentic shopping experiences.