The Music Promotion team is building products that allow creators to promote their work to reach new audiences and create lasting connections with their fans. We’re looking for a Machine Learning Engineer to help us build systems that more accurately understand the performance that promotion can have, giving customers actionable insights for building their promotion strategies, whether it’s a DIY artist or an industry-facing partner.
As an ML Engineer, you will help execute on strategies for understanding the factors that play a role in the performance of promoted tracks across the globe. You’ll build data-driven solutions, as well as effective online and offline strategies to efficiently iterate and evaluate model approaches. You’ll have access to a growing list of datasets, features and ML infrastructure to continually experiment and improve the model-based approach.
What You'll Do
- Contribute to the design, build, evaluation, shipping, and refinement of systems that improve Spotify’s promotional performance with hands-on ML development
- Collaborate with a multidisciplinary team to optimize machine learning models for production use cases, ensuring they are highly efficient, scalable, and consistently meet well-defined success criteria
- Influence the technical design, architecture, and infrastructure decisions to support new and diverse machine learning architectures.
- Work with Data and ML Engineers to support transitioning machine learning models from research and development into production
- Implement and monitor model success metrics, diagnose issues, and contribute to an on-call schedule to maintain production stability.
Who You Are
- You have experience implementing ML systems at scale in Java, Scala, Python or similar languages as well as experience with ML frameworks such as TensorFlow, PyTorch, etc.
- You have an understanding of how to bring machine learning models from research to production and are comfortable working with innovative, cutting-edge architectures.
- You have a collaborative mindset, enjoy working closely with research scientists, machine learning engineers, and data engineers to innovate and improve models.
- You have experience in optimizing machine learning models for production use cases
- You preferably have experience with data pipeline tools like Apache Beam, Scio, and cloud platforms like GCP
- You have some exposure to causal ML models, including things like counterfactuals.
- You are familiar with creating model success metric dashboards, diagnosing production issues, and are willing to take part in an on-call schedule to maintain performance.
Where You'll Be
Skills Required
- Experience implementing ML systems at scale
- Proficiency in Java, Scala, Python or similar languages
- Experience with ML frameworks such as TensorFlow, PyTorch
- Understanding of transitioning ML models from research to production
- Experience optimizing machine learning models for production
Spotify Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Spotify and has not been reviewed or approved by Spotify.
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Flexible Benefits — Employees consistently praise the total compensation package beyond base salary, highlighting a mix of RSUs, cash incentives, and stipends alongside core pay. The package is described as flexible and customizable through equity choices (e.g., RSUs, options, cash) that can be tailored for long-term wealth building.
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Leave & Time Off Breadth — Time-off offerings are repeatedly highlighted as substantial, including generous vacation, paid sick days, volunteer time, and flexible holidays. These policies are framed as a meaningful part of the overall rewards experience beyond salary.
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Healthcare Strength — Health coverage is portrayed as comprehensive, spanning medical, dental, vision, life insurance, disability coverage, and mental health support. Additional employer contributions to HSAs are cited as strengthening the overall health and wellness value proposition.
Spotify Insights
What We Do
Spotify transformed music listening forever when it launched in Sweden in 2008. Discover, manage and share over 50m tracks for free, or upgrade to Spotify Premium to access exclusive features including offline mode, improved sound quality, and an ad-free music listening experience.
