Applied Science Intern: ML Knowledge Management

Amazon Inc.; Seattle (WA); On-site; USD 122,000 - 149,000; Full time

Job summary

Amazon seeks a 2027 Summer Applied Science Intern in Information & Knowledge Management within Machine Learning, Seattle, WA. You will help build systems to discover, reuse, and collaborate on ML models and datasets using NLP and information retrieval on large unstructured data stores.

You will work with cutting-edge tools, collaborate across teams, and contribute to ML operations, knowledge engineering, and data-driven decisions. PhD candidate and full-time summer availability required.

Qualifications

• Must be enrolled in a PhD program
• Relocation to internship base required
• Experience programming in Java, C++, Python or related language
• Experience with Knowledge Graphs and Extraction, Neural Networks/GNNs, Data Structures and Algorithms, Time Series, ML, NLP, DL, LLMs, Graph Modeling
• Must be available for full-time (40 hours per week) internship for the whole duration
• Evidence of science depth through publications or thesis

Responsibilities

• Develop scalable ML frameworks and tools for knowledge management in ML
• Collaborate with cross-functional teams to advance ML assets lifecycle
• Research best practices in ML operations, knowledge engineering, and information management
• Design and evaluate novel approaches to enhance Amazon's ML capabilities
• Think about customers and how to improve the customer delivery experience

Skills

Knowledge Graphs and Extraction

Neural Networks/GNNs

Time Series

Machine Learning

Natural Language Processing

Deep Learning

Large Language Models

Graph Modeling

Programming/Scripting Languages

Education

PhD student

Tools

Java

C++

Python

Job description

Amazon seeks a 2027 Summer Applied Science Intern in Information & Knowledge Management within Machine Learning, Seattle, WA. You will help build systems to discover, reuse, and collaborate on ML models and datasets using NLP and information retrieval on large unstructured data stores.

You will work with cutting-edge tools, collaborate across teams, and contribute to ML operations, knowledge engineering, and data-driven decisions. PhD candidate and full-time summer availability required.