We are looking for interns who can work with one of our teams onsite during the fall term. You must be available for 12-16 weeks anytime from August 1 - Dec 31.
Are you experienced at applying deep learning model to big-data sets? Are you excited by analyzing and modeling terabytes of text, images, and other types of data to solve real-world problems? We love data and we have lots of it. We’re looking for an excited researcher capable of using deep learning and statistical techniques to create state-of-the-art solutions to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems.
Internships last 12-20 weeks and start year round. In order to be considered for an internship you must be enrolled at a university and plan to return for additional school terms prior to graduating.
Current enrollment in a degree-granting college or university working towards a Master’s or Ph.D. degree in Engineering, Computer Science, Machine Learning, Math, Statistics or related field.
Strong background in machine learning with domain knowledge and experience in the following areas: data-driven statistical modeling, graphical models, feature extraction and analysis, supervised learning, in particular discriminative methods.
Theory and practice of Design of Experiments and statistical analysis of results.
An understanding of machine learning, algorithms and computational complexity.
Programming skills sufficient to extract, transform, and clean large (multi-TB) data sets in a Unix/Linux environment.
Skills with Java, C++, or other programming language, as well as with R, MATLAB, Python or similar scripting language
Ability to relate to and solve business problems through machine learning, data mining and statistical algorithms.
Strong desire to push your ideas into production, overcoming obstacles, in order to benefit Amazon's customers.
Familiar with the core undergraduate curriculum of Computer Science.
Algorithm development experience
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
Familiar with the techniques and limitations of observational studies.
Familiar with theory and practice of information retrieval, relevance, machine learning, and data mining.
Skilled at data visualization and presentation.
Excellent critical thinking skills, combined with the ability to present your beliefs clearly and compellingly in both verbal and written form.
Energy and willingness to formulate, test, and discard or revise many hypotheses to help Amazon improve its product catalog.