Spotify
Experience
SeniorMachineLearningEngineer-ContentIntelligence
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Machine Learning Engineer - Content Intelligence at Spotify. Skills: Machine Learning, Content Intelligence, LLM, AI. Lead ML initiatives. Design ML systems”
Industry & Context.
Technical trade-offs
What They're Looking For.
Must Have
Solid experience developing ML systems, Delivered large-scale ML architectures, Deep experience ML/deep learning/AI, Hands-on LLM experience, Experience building evaluation frameworks, Experience influencing technical direction, Experience mentoring engineers
Nice to Have
Experience with NLP, Prompt engineering experience, Retrieval-augmented generation experience, Vector databases experience, Multimodal ML systems experience, Experience integrating AI-assisted development tools
What You'll Do.
Advance NLP capabilities
Advance multimodal AI capabilities
Advance content intelligence capabilities
Build LLM-powered solutions
Evaluate LLM-powered solutions
Define evaluation methodologies
Partner with Product Managers
Partner with Engineering Managers
Partner with Staff Engineers
Partner with Data Scientists
Influence technical strategy
Influence roadmap decisions
Elevate ML engineering standards
Elevate ML engineering best practices
Contribute to AI-assisted development adoption
Improve team productivity
Improve engineering effectiveness
How You'll Work.
Team & Collaboration
Cross-functional teams; Product Managers; Engineering Managers; Staff Engineers; Data Scientists
Communication Scope
Communicate complex concepts
Process & Methodology
Technical strategy, Roadmap decisions
Full Job Description
## What You'll Do Lead end-to-end machine learning initiatives from ideation and prototyping through experimentation, deployment, and large-scale productionization. Design, develop, and deploy machine learning systems that operate across hundreds of millions of content signals using both real-time and batch processing architectures. Advance Spotify’s capabilities in natural language understanding, multimodal AI, and content intelligence.Build and evaluate LLM-powered solutions using modern prompting techniques, retrieval systems, and advanced model orchestration approaches. Define rigorous evaluation methodologies including golden datasets, precision and recall frameworks, offline benchmarking, and online experimentation. Partner closely with Product Managers, Engineering Managers, Staff Engineers, and Data Scientists to influence technical strategy and roadmap decisions. Mentor engineers across the organization and help elevate machine learning engineering standards and best practices. Contribute to the adoption of AI-assisted development workflows and tooling that improve team productivity and engineering effectiveness. ## Who You Are You have solid experience developing and deploying machine learning systems in production environments. You have successfully delivered large-scale machine learning architectures operating on substantial datasets and high-throughput production systems. You have deep experience with machine learning, deep learning, and modern AI technologies. You have hands-on experience working with large language models and understand how to evaluate, adapt, and deploy them effectively for real-world product challenges. You have experience building evaluation frameworks and can quantify model performance through robust experimentation and measurement techniques. You know how to navigate ambiguity and make thoughtful technical trade-offs that balance product impact, scalability, and engineering quality. You have experience influencing technical direc
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