Are you a MS or PhD student interested in a 2021 Applied Science Internship in the fields of Natural Language Processing, Search, Machine Learning?


Do you enjoy diving deep into hard technical problems and coming up with solutions that enable successful products that improve the lives of people in a meaningful way?


If this describes you, come join our research teams at Amazon. As an Applied Science Intern, you will have access to large datasets with billions of images and video to build large-scale machine learning systems. Additionally, you will analyze and model terabytes of text, images, and other types of data to solve real-world problems and translate business and functional requirements into quick prototypes or proofs of concept.
We are looking for smart scientists capable of using a variety of domain expertise combined with machine learning and statistical techniques to invent, design, and implement state-of-the-art solutions for never-before-solved problems.


BASIC QUALIFICATIONS

· Enrolled in a Master’s or Ph.D. degree in engineering, computer science, machine learning, operations research, statistics, or related fields.
· Experience in designing experiments and statistical analysis of results.
· Experience in understanding and ability to implement algorithms using both toolkits and self-
· developed code.
· Experience with Java, C++, or other programming language, as well as with R, MATLAB, Python, or similar scripting language.


PREFERRED QUALIFICATIONS

· Enrolled in Ph.D. degree in engineering, computer science, machine learning, operations research, statistics, or related fields.
· Familiar with the core undergraduate curriculum of computer science.
· Technical fluency; comfort understanding and discussing architectural concepts and algorithms,
· schedule tradeoffs and new opportunities with technical team members.
· Publications at top-tier peer-reviewed conferences or journals.
· Excellent critical thinking skills, combined with the ability to present your beliefs clearly and compellingly in both verbal and written form.

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