Machine Learning Engineer - Content Understanding

November 17

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Spotify

WebsiteLinkedInAll Job Openings

5001 - 10000 employees

Founded 2008

📱 Media

👥 B2C

🛍️ eCommerce

Description

• As Spotify grows its video catalog, understanding and classifying visual content in our catalog becomes very important to support moderation, search and recommendation use cases. • We are a small, cross-functional team of Machine Learning Engineers and Data Engineers leveraging state of the art machine learning solely focused on building and deploying Audio understanding models. • Delivering the best Spotify experience possible. • To as many people as possible. • In as many moments as possible. • That’s what the Experience team is all about. • We use our deep understanding of consumer expectations to enrich the lives of millions of our users all over the world, bringing the music and audio they love to the devices, apps and platforms they use every day. • Know what our users want? Join us and help Spotify give it to them. • As a Machine Learning Engineer in our Content Understanding teams, you will help define and build ML deployed at scale in support of a broad range of use cases driving value in media and catalog understanding.

Requirements

• You have professional experience in applied machine learning • Extensive experience working in a product and data-driven environment (Python, Scala, Java, SQL, or C++, with Python experience required) and cloud platforms (GCP or AWS) • You have some hands-on experience implementing or prototyping machine learning systems at scale • You have experience architecting data pipelines and are self-sufficient in getting the data you need to build and evaluate models, using tools like Dataflow, Apache Beam, or Spark • You care about agile software processes, data-driven development, reliability, and disciplined experimentation • You have experience and passion for fostering collaborative teams • Experience with TensorFlow, pyTorch, and/or Google Cloud Platform is a plus • Experience with building data pipelines and getting the data you need to build and evaluate your models, using tools like Apache Beam / Spark is a plus