Builds and scales ML systems for content understanding across audio, video, text, and images at Spotify. Develops models for classification, tagging, semantic analysis, and enrichment using LLMs, enabling automation, quality, and new product features.
Salary not listed
HybridML Engineering
About the role
What You Will Do
Build and scale machine learning systems that generate deep understanding of content across modalities
Develop models for classification, tagging, semantic understanding, and content enrichment
Create high quality content enrichment at scale using LLMs and agentic systems
Design systems that make content intelligence signals available to downstream teams and products
Improve automation for content quality, safety, and metadata enrichment at scale
Collaborate with product, policy, and engineering teams to translate content intelligence into user impact
Contribute to evaluation frameworks, data pipelines, and annotation systems
Support rapid experimentation to prototype and launch new types of content signals
Help improve system reliability, scalability, and performance across large datasets
Who You Are
Experience building and deploying machine learning systems in production
Comfortable working with ML frameworks such as PyTorch, TensorFlow, or similar
Experience working with large datasets and care about data quality and evaluation
Interested in or have worked with multimodal machine learning
Understand how to design systems that balance automation with quality and user experience
Comfortable working on complex problems with evolving requirements
Think in systems and understand how models connect to product outcomes
Communicate clearly and work well across technical and non-technical teams
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