Senior Machine Learning Engineer - Content Intelligence

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

• Solid experience developing and deploying machine learning systems in production environments.
• Successfully delivered large-scale machine learning architectures operating on substantial datasets and high-throughput production systems.
• Deep experience with machine learning, deep learning, and modern AI technologies.
• Hands-on experience working with large language models and understand how to evaluate, adapt, and deploy them effectively for real-world product challenges.
• Experience building evaluation frameworks and can quantify model performance through robust experimentation and measurement techniques.
• Know how to navigate ambiguity and make thoughtful technical trade-offs that balance product impact, scalability, and engineering quality.
• Experience influencing technical direction across cross-functional teams and can communicate complex machine learning concepts to diverse audiences.
• Care about developing others and enjoy mentoring engineers through technical guidance and collaboration.
• Experience working with NLP, prompt engineering, retrieval-augmented generation (RAG), vector databases, or multimodal machine learning systems.
• Curious about emerging AI technologies and excited about integrating tools such as Claude Code, Cursor, and other AI-assisted development capabilities into engineering workflows.