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Spotify's Personalization team is looking to solve the business problem of making content discovery easier and more enjoyable for listeners by improving recommendation systems and understanding user satisfaction drivers.

Requirements

• strong background in machine learning, enjoy applying theory to develop real-world applications, with expertise in statistics and optimization, especially in sequential models, transformer architecture models, and fine-tuning processes for sequential models
• hands-on experience with large cross-collaborative machine learning projects and managing stakeholders
• hands-on experience implementing production machine learning systems at scale in Java, Scala, Python, or similar languages
• Experience with TensorFlow, PyTorch, Scikit-learn, etc. is a strong plus
• some experience with large scale, distributed data processing frameworks/tools like Apache Beam, Apache Spark, or even our open source API for it - Scio, and cloud platforms like GCP or AWS

Responsibilities

• Contribute to designing, scaling/building, evaluating, integrating, shipping, and refining ML models for the personalization of the main homepage
• Lead collaborations and align across PZN to integrate and A/B test mid-term signals in various recommendation systems
• Promote and role-model best practices of ML systems development, testing, evaluation, etc., both inside the team as well as throughout the organization.

Other

• The United States base range for this position is $176,166 $251,666 plus equity.
• The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, paid sick leave.
• You care about agile software processes, data-driven development, reliability, and disciplined experimentation.