2020 Machine Learning Internship - Core AI

Job description

DESCRIPTION

We are looking for Master's and PhD students to join Amazon CoreAI in Berlin for a 3-6 month internship in 2020.

Our team develops innovative machine learning-based solutions for a range of applications in economics, planning, and logistics. We focus on methods from the field of probabilistic modelling and inference with emphasis on scalability and robustness. 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 our team 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.

PREFERRED QUALIFICATIONS

· Ability to convey rigorous mathematical concepts and considerations to non-experts.
· Ability to distill problem definitions, models, and constraints from informal business requirements, and to deal with ambiguity and competing objectives.
· Good software development skills.
· Publications in top-tier science conferences and journals.
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 graduate or postgraduate degree in Machine Learning, Data Science, Applied Statistics or in a related field.
· Hands-on experience in probabilistic modelling/Bayesian inference.
· Enthusiasm for applying machine learning to real-world problems.
· Computer science grounding in a range of algorithms and data structures.
· Programming experience in Python.
· Ability to present your beliefs clearly and compellingly in both verbal and written form