Machine learning has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations. We have the richest, most diverse data of any Internet company. We also have a very broad collection of practical problems where Learning systems can dramatically improve the customer experience, reduce cost, and drive speed and automation. We believe in the long-term impact of Machine Learning to all our businesses.
The ML team within AWS provides opportunities to innovate in a start-up mode and lets you contribute to game-changing projects and technologies that get deployed on device or cloud. As a scientist in the AWS-ML team, you'll partner with technology and business teams to build new services that surprise and delight our customers. You will be working with terabytes of text, images, and other types of data to solve real-world problems. You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. Research science at Amazon is a highly experimental activity. Besides theoretical analysis and innovation, our scientists also work closely with software engineers to put algorithms into practice. They also work on cross-disciplinary efforts with other scientists within Amazon.
We’re looking for top scientists capable of using ML and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems. We have multiple positions available for applied scientists in Seattle, Palo Alto and Sunnyvale for machine learning experts at all career stages, from graduating doctoral students to internationally renowned researchers and practitioners.
The primary responsibilities of this role are to:
· Use deep learning, machine learning and analytical techniques to create scalable solutions for business problems
Design, development and evaluation of highly innovative models for predictive learning, content ranking, and anomaly detection
· Interact with customer directly to understand the business problem, help and aid them in implementation of DL/ML algorithms to solve problems
· Analyze and extract relevant information from large amounts of historical data to help automate and optimize key processes
Work closely with account team, research scientist teams and product engineering teams to drive model implementations and new algorithms
Research and implement novel deep learning, machine learning and statistical approaches