2020 Machine Learning Internship - Intelligent Cloud Control

Job description

DESCRIPTION

We are looking for PhD students to join Amazon Research in Berlin for a 3-6 month internship in 2020.

Amazon's Intelligent Cloud Control Group in Berlin is looking for an Applied Science Intern to develop models that will revolutionize the way Amazon manages its cloud computing infrastructure. With an ever-growing number of fleets, developers, customers, products, marketplaces, sellers, and businesses the Amazon service graph is one of the largest and most complex technical ecosystems in the world. We are building an Intelligent Cloud Control system that enables Amazon businesses (Retail, Amazon Video, Kindle, and more) to accelerate innovation in the cloud.

As a science intern in the Intelligent Cloud Control team, you'll partner with other scientists and machine learning engineers to build new models that delightfully surprise our customers. We develop cutting edge algorithms that involve learning from large datasets such as monitoring telemetry and logs from every host at Amazon. You will uncover critical insights from these vast datasets, develop prototypes to evaluate the viability of new approaches, and identify opportunities to create data products from these prototypes. You will collaborate with software development engineers to understand the human and automated processes that govern the operation of one of the world's largest websites, and build models to help us make decisions from data.

You will work on mathematical and data problems with a large element of unpredictability. You will analyze and process large amounts of data to find insights that impact every development team at Amazon. You will develop sophisticated algorithms and improve existing approaches using statistical models, machine learning algorithms, and big data solutions. The models you deliver will revolutionize the way that the Amazon website responds to operational issues, leveraging the most recent advances in machine learning and distributed computing to build automatic remediation systems that cut through the noise and surface emergent issues in real-time. As part of our team, you will utilize and be exposed to AWS technologies and contribute to research publications within and outside of Amazon.

We’re looking for a science intern capable of using machine learning and other quantitative techniques to design, evaluate, and prototype state-of-the-art solutions for never-before-solved problems.

PREFERRED QUALIFICATIONS

· Significant peer reviewed scientific contributions in relevant fields
· Strong software development skills
· Practical experience applying ML to solve complex problems
· Strong personal interest in learning, researching, and creating new technologies with high business impact
· Ability to distill problem definitions, models, and constraints from informal business requirements, and to deal with ambiguity and competing objectives
We offer flexible start dates throughout the whole year. Please note that we expect to experience a high volume of applications and we will fill positions as they become available, so we encourage you to apply early. Please include your academic transcript in your CV.
Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills.

Desired profile

BASIC QUALIFICATIONS

· On-track for PhD degree in Computer Science, Operations Research, Statistics, Engineering or Mathematics
· Hands-on experience and expertise in one or more of the following areas: forecasting, anomaly detection, deep learning, high-dimensional time series analysis, reinforcement learning, and/or information retrieval
· Strong technical knowledge in algorithms and data structures
· Proficiency in programming/scripting languages; experience in Python is a plus
· Enthusiasm for applying machine learning to real-world problems
· Ability to present your ideas clearly and compellingly in both verbal and written form