Senior Machine Learning Engineer

We design Spotify’s consumer experience—end to end, moment to moment, across every screen, platform, and partner integration. Our mission is to make listening feel effortless, personal, and joyful for billions of users around the world. That means turning complexity into clarity across hundreds of touchpoints—from our mobile and desktop apps to the smart speakers, TVs, cars, and integrations where Spotify shows up every day. If it touches a consumer, we shape it. We bring deep insight into human behavior, design, and technology to craft experiences that feel intuitive, expressive, and unmistakably Spotify.

The Verbatim squad sits within the Enrichment & Content Intelligence product area and is focused on helping Spotify better understand audio, text, and visual content through machine learning. The team develops technologies that power experiences across Spotify including content skipping, transcription, moderation, and visual understanding. Working at the intersection of large-scale machine learning and product innovation, the squad partners closely with Product, Engineering, and Data Science teams to build intelligent systems that improve how users experience content across the platform.

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 direction across cross-functional teams and can communicate complex machine learning concepts to diverse audiences.

You care about developing others and enjoy mentoring engineers through technical guidance and collaboration.

You have experience working with NLP, prompt engineering, retrieval-augmented generation (RAG), vector databases, or multimodal machine learning systems.

You are curious about emerging AI technologies and excited about integrating tools such as Claude Code, Cursor, and other AI-assisted development capabilities into engineering workflows.

Where You'll Be

This role is based in London or Stockholm.

We offer you the flexibility to work where you work best! There will be some in person meetings, but still allows for flexibility to work from home.

Senior Machine Learning Engineer - Content Intelligence

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What You'll Do

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