We are looking for a Machine Learning Engineer for the team that supplies our close friends in Spotify Customer Support with the software solutions they need. These are customer facing products (e.g. support.spotify.com), internally built systems, and integrations with our vendors. We are about 40 people in total across the development team for Customer Experience, with people in New York, Stockholm and London. This position is based in New York.
What you’ll learn and do
- Contribute to designing, building, evaluating, shipping, and refining Spotify’s support products by hands-on ML development
- Collaborate with cross functional agile teams spanning user research, design, data science, product management, and engineering to build new product features that advance our mission to support artists and fans in personalized and relevant ways
- Prototype new approaches and production-ize solutions at scale for our hundreds of millions of active users
- Help drive optimization, testing, and tooling to improve quality
- Be part of an active group of machine learning practitioners in New York (and across Spotify) collaborating with one another
Who you are
- You have a strong background in machine learning, with experience in personalization and Natural Language Processing.
- You have hands-on experience implementing production machine learning systems at scale in Java, Scala, Python, or similar languages. Experience with XGBoost, TensorFlow is also a plus.
- You preferably have experience with data pipeline tools like Apache Beam or even our open source API for it, Scio and cloud platforms like GCP or AWS.
- You care about agile software processes, data-driven development, reliability, and disciplined experimentation
- You love your customers even more than your code
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.