{ “@context”: “http://schema.org”, “@type”: “JobPosting”, “title”: “Staff Machine Learning Engineer (Content Intelligence)”, “description”: “
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
Company: Deepstreamtech
Location: London
Job Description:
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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Posted: May 20th, 2026