Staff Machine Learning Engineer – Content Intelligence

Requirements

• You have experience building and deploying machine learning systems in production
• You are comfortable working with ML frameworks such as PyTorch, TensorFlow, or similar
• You have experience working with large datasets and care about data quality and evaluation
• You are interested in or have worked with multimodal machine learning
• You understand how to design systems that balance automation with quality and user experience
• You are comfortable working on complex problems with evolving requirements
• You think in systems and understand how models connect to product outcomes
• You communicate clearly and work well across technical and non-technical teams

What the job involves

• We’re seeking a Staff Machine Learning Engineer to build and scale foundational ML systems that power content understanding across Spotify
• In this role, you’ll work on systems that generate deep, machine-readable understanding of content across audio, video, text, and images: enabling automation, improving quality, and unlocking new product experiences
• This work is central to delivering safe, high-quality, and differentiated experiences for millions of listeners and creators worldwide
• 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

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