Experienced Machine Learning Engineer – Growth

Spotify
Spotify

Job Overview

What you’ll do

  • Work with a high-performance, cross-functional team whose mission is to engage users and communicate the many value propositions of Spotify to each user, from their favorite artist’s new album to new music discovery, from keeping up with favorite podcasts, to finding live performances, and more.
  • Work at the intersection of engineering and marketing, crafting automated and optimized marketing systems to operate a best-in-class business that grows Spotify’s global user base.
  • Train models and evaluate their effectiveness against metrics that encapsulate real and immediate business objectives.
  • Drive tooling and infrastructure improvements for cloud-based ML deployments
  • Apply techniques and methods from literature to business operations that require forecasting, classification, ranking, and A/B/N testing.

    • Who you are

  • You have strong hands-on industry experience implementing and maintaining high-scale, production ML systems.
  • You are comfortable explaining the intuition and assumptions behind ML and mathematical concepts, and you can creatively apply these concepts to craft ML systems to seek challenging problems. You can explain and discuss pros and cons of learning regimes, and identify scenarios in which one might best apply.
  • You are experienced with crafting data pipelines, and you are self-sufficient in driving the process of designing features, labeling training data, and evaluating models.
  • You care about agile software processes, data-driven development, reliability, and focused experimentation
  • You are passionate about learning and sharing in an early-stage team environment.
  • You love your customers even more than your code

    • Plus/bonus:

  • Experience with TensorFlow.
  • Experience in causal inference or methods related to customer lifetime value
  • Experience with applying deep learning techniques for ranking, content affinity, sequential modeling, or natural language processing in production
  • Experience with high-scale, distributed data processing frameworks like Beam or Spark
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