2019 Internship in Machine Learning, Computer Vision, Reinforcement Learning or Natural Language Processing

Job description

DESCRIPTION

We are looking for Master's and PhD students to join Amazon Research in Berlin for a 3-6 month internship in 2019.

Our international teams develop innovative machine learning-based solutions for a broad range of application domains such as computer vision, NLP, robotics, machine translation, forecasting, music personalization, and many more. In addition to developing application-specific solutions, we focus on research in active learning, online learning, transfer learning, continual learning, deep learning, probabilistic modeling & programming, and data quality enhancement. To this end, we collaborate closely with other science and product teams across Amazon as well as local universities and the open-source community.

As part of the internship you will be a full member of one of our teams working with other scientists and software development engineers on completing a self-contained internship project. Specific challenges will be dealing with very large heterogenous datasets, designing novel algorithms that are robust and reliable, and efficient implementations that can run in production. Some internship projects also include the goal of publishing findings or open-sourcing code developed during the internship.

Desired profile

BASIC QUALIFICATIONS

· On-track for graduate or postgraduate degree in Machine Learning, Data Science, Applied Statistics or in a related field.
· Hands-on experience in one or more of the following areas: deep learning, probabilistic modelling, statistical machine learning, data processing & data analytics, or application-specific expertise in CV, NLP, or Robotics.
· Enthusiasm for applying machine learning to real-world problems.
· Computer science grounding in a range of algorithms, data structures, and programming languages; experience in Python is a plus.
· Ability to present your beliefs clearly and compellingly in both verbal and written form.