Applied Scientist – GenAI & Multimodal Catalog Systems

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

Job summary

Amazon.com Services LLC is seeking an innovative applied scientist for the Item and Relationship Platform to build state-of-the-art algorithms that infer product-to-product relationships across billions of products. You will work with GenAI, VLMs, and multimodal data to enhance catalog understanding and customer experiences.

You will partner with tech and business leaders to deploy scalable models in a fast-paced environment, mentor peers, publish findings, and push the frontier of GenAI

Qualifications

• PhD or Master’s degree with 4+ years in CS, CE, ML or related field.
• Experience programming in Java, C++, or Python.

Responsibilities

• Formulate novel research problems at the intersection of GenAI, multimodal learning, and large-scale information retrieval.
• Design models using VLMs, foundation models, and agentic architectures for product identity and catalog understanding at scale.
• Develop explainable AI methods balancing performance and scalability for production systems.
• Own end-to-end ML pipelines from ideation to production, processing petabytes of multimodal data.
• Define research roadmaps aligned with business priorities for foundational and incremental improvements.
• Mentor scientists and engineers on ML techniques and experimental design.
• Represent the team in the science community with publications and talks.

Skills

PhD or Master's + 4+ years CS/CE/ML

Java

C++

Python

Education

PhD

Master's degree in CS/CE/ML

Job description

Amazon.com Services LLC is seeking an innovative applied scientist for the Item and Relationship Platform to build state-of-the-art algorithms that infer product-to-product relationships across billions of products. You will work with GenAI, VLMs, and multimodal data to enhance catalog understanding and customer experiences.

You will partner with tech and business leaders to deploy scalable models in a fast-paced environment, mentor peers, publish findings, and push the frontier of GenAI