Machine Learning Infrastructure Engineer

Spotify   •  

New York, NY

Not Specified years

Posted 177 days ago

This job is no longer available.

We are looking for a Senior Software Engineer to help us define and build the next generation of ML infrastructure at Spotify.  Our mission is to enable every team at Spotify to iterate quickly on hypotheses and scale their experiments to data sets with hundreds of billions of data points.  In this role you will work closely with many of the ML teams at Spotify across missions including ads targeting, personalization, music recommendations, pricing and more.  Above all, your work will impact the way the world experiences music.

What you’ll do

  • Build infrastructure to apply machine learning methods to massive data sets in production environments
  • Collaborate with cross functional agile teams of software engineers, data engineers, ML experts, and others in building new product features
  • Contribute to new and existing Spotify open source machine learning and data processing products (scio, featran, zoltar)
  • Leverage your experience to drive best practices in ML and data engineering
  • Gain a deep understanding of various models (collaborative filtering, NLP, deep learning) in order to understand their tradeoffs and bottlenecks
  • Design machine learning platforms and pipelines for training and running machine learning models on distributed systems
  • Determine the feasibility of projects through quick prototyping with respect to performance, quality, time and cost using Agile methodologies

Who you are

  • You have development experience with an object-oriented programming language such as C++ or Java and/or functional programming languages
  • You have previous industry experience with ML systems using frameworks such as Scikit-learn and Tensorflow
  • You have previously built APIs and libraries for Java, Scala or Python
  • You care about agile software processes, data-driven development, reliability, and responsible experimentation
  • You preferably have experience with data processing and storage frameworks like Google Cloud Dataflow, Hadoop, Scalding, Spark, Storm, Cassandra, Kafka, etc.
  • You preferably have machine learning publications or open source contributions to share with us
  • Skilled communicator and have a proven record of leading work across disciplines