Software Development Engineer (Machine Learning and Big Data)

Amazon
Amazon

Job Overview

DESCRIPTION

Do you want to be part of a team dedicated to building machine learning applications that process billions of dollars a day using the latest technology? Do you want to measure the impact of your work in terms of millions of dollars?
In Finance Technology, we build machine learning systems that look through billions of dollars in transactions from across Amazon. Our goal is to increase the adoption of machine learning across Finance. To do this, we build machine learning applications which use the latest techniques for forecasting, classification, and anomaly detection purposes. Our current applications range from finding anomalies in data all the way to predicting how much cash to keep in an account.
In a typical day, you will work with machine learning scientists, other software engineers, and business groups across Amazon Finance. You will work with terabytes of data and develop machine learning pipelines which process billions of dollars. We partner with our customers, so you will meet with them directly to gain direct feedback on your work. Our team and customer are comfortable trying new ideas, so you will test your ideas in the real world.

BASIC QUALIFICATIONS

· Programming experience with at least one modern language such as Java, C++, or C# including object-oriented design
· 1+ years of experience contributing to the architecture and design (architecture, design patterns, reliability and scaling) of new and current systems.
· 2+ years of non-internship professional software development experience

PREFERRED QUALIFICATIONS

· MSc in computer science or a related field
· Java, Scala, or Python experience
· Experience with Machine Learning algorithms (ex: Random Forest, clustering, etc.)
· Experience with Big Data
· Experience developing cloud software services and an understanding of design for scalability, performance and reliability
Amazon.com is an Equal Opportunity-Affirmative Action Employer – Age / Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.

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