Machine Learning Engineer
location_onHybrid
Job Description
About the Team
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.
About the Role
We are 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.
Our Culture & Mission
Spotify transformed music listening forever when we launched in 2008. Our mission is to unlock the potential of human creativity by giving a million creative artists the opportunity to live off their art and billions of fans the chance to enjoy and be passionate about these creators. Everything we do is driven by our love for music and podcasting. Today, we are the world’s most popular audio streaming subscription service.
Spotify is an equal opportunity employer. You are welcome at Spotify for who you are, no matter where you come from, what you look like, or what’s playing in your headphones. Our platform is for everyone, and so is our workplace. The more voices we have represented and amplified in our business, the more we will all thrive, contribute, and be forward‑thinking! So bring us your personal experience, your perspectives, and your background. It’s in our differences that we will find the power to keep revolutionizing the way the world listens.
At Spotify, we are passionate about inclusivity and making sure our entire recruitment process is accessible to everyone. We have ways to request reasonable accommodations during the interview process and help assist in what you need. If you need accommodations at any stage of the application or interview process, please let us know—we’re here to support you in any way we can.
Work location
Work model: Hybrid
Hybrid
Key Responsibilities
- check_circleDesign, build, and refine systems to improve promotional performance
- check_circleOptimize machine learning models for production use cases
- check_circleInfluence technical design and infrastructure decisions
- check_circleTransition machine learning models from research to production
- check_circleImplement and monitor model success metrics
- check_circleDiagnose production issues and maintain on-call stability
Requirements
- verifiedExperience implementing ML systems at scale in Java, Scala, Python, scale in Java, Scala, Python or similar languages
- verifiedScala, Python or similar languages
- verifiedExperience with ML frameworks such as TensorFlow, PyTorch, etc.
- verifiedas TensorFlow, PyTorch, etc.
- verifiedUnderstanding of bringing ML models from research to production
- verifiedproduction
- verifiedExperience optimizing ML models for production use cases
- verifiedcases
- verifiedExperience with data pipeline tools like Apache Beam,
- verifiedlike Apache Beam, Scio, and cloud platforms like GCP
- verifiedio, and cloud platforms like GCP
- verifiedExposure to causal ML models, including counterfactuals
- verifiedExperience creating model success metric dashboards
- verifieddashboards
- verifiedExperience diagnosing production issues
Benefits & Perks
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