Machine Learning Infrastructure Lead at Spotify
New York City, NY, US
Spotify’s machine learning infrastructure team is looking for a lead engineer to join our team. Our team is responsible for developing and maintaining Spotify’s frameworks and infrastructure for developing and running advanced machine learning data pipelines. These include the algorithms which drive Spotify’s Discover Weekly and Release Radar features. We try to open source our work as much as we can, some prominent examples are Scio, Luigi, and Annoy.

What you’ll do
Build and maintain tools and frameworks used by our machine learning engineers to build some of Spotify’s most prominent features.
Open source your work and contribute to existing open source projects like Apache Beam, Apache Spark, Scalding, or Apache Hadoop.
Research new tools for accelerating machine learning development and provide guidance to teams in Spotify like Apache Zeppelin, TensorFlow, and Google Cloud ML.
Support the teams that are using the infrastructure in production.
Have had opportunities to talk about your work internally and externally in meet-ups and conferences.
Collaborate closely with other engineers and become a valued member of an autonomous, cross-functional team.

Who you are
You have contributed to an open source project that relates to machine learning and/or big data processing.
You have earned at least a B.A. in computer science, mathematics, statistics, physics, psychology, or related area.
You are well-versed in data-driven and data-informed product development.
You have experience in fostering a strong engineering culture in an agile environment.
You preferably, have publicly spoken about your work in meetups and/or conferences.

We are proud to foster a workplace free from discrimination. We strongly believe that diversity of experience, perspectives, and background will lead to a better environment for our employees and a better product for our users and our creators. This is something we value deeply and we encourage everyone to come be a part of changing the way the world listens to music.