We are looking for Research Scientist Interns for Spotify’s research lab based in London to work on a number of interesting problems in machine learning, deep learning, recommendations, user modeling/engagement, and large scale experimentation. We are part of a high impact team that is building the next generation technologies aimed at making every user interaction with Spotify amazing through personalization and discovery.
We’re looking for talented research interns who have applied experience in the field of Machine Learning, Machine Intelligence, User Behavioral Analysis, IR, NLP, and more broadly, AI.
The User Engagement team works on some of Spotify’s key features – personalized playlists such as Discover Weekly and Daily Mix, the Home view, and Search. Our projects are intended to take on some of technology’s greatest challenges and make impact on millions of users. Some of the challenges our team is working on include developing terascale solutions for understanding and interpreting user interaction signals, understanding user success with short term & long term metrics, developing algorithmically curated playlists and other challenges in machine learning and user understanding.
Who you are
You are currently enrolled in a PhD programme in Computer Science, Data Science, or related areas with a strong computational focus.
You will have a strong knowledge of data mining, machine learning or evaluation with experience in machine learning, deep learning, optimization techniques, information retrieval and/or natural language understanding.
You have publications in communities such as WWW, SIGIR, WSDM, RecSys, CHI, KDD, AAAI, ACL, NIPS, ICML, UbiComp, or related, in the following topics:
user understanding: music cognition, metrics and evaluation, large scale experimentation
matching: information retrieval, recommendation, machine learning
You possess solid hands-on skills in sourcing, cleaning, manipulating, analyzing, visualizing and modeling of large scale data.
Spotify, its easy to find the right music for every moment on your phone, your computer, your tablet and more.