ML Engineer

Work with other senior and staff engineers on 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., About the position The Euler 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 AI and spam detection, kids content classification, entity linking and creation, and more. 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

• Work with other senior and staff engineers on 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.

• 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 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.

qualifications:

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 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.

skills:

AI,multimodal AI,retrieval systems,batch processing,Claude Code,Cursor,deep learning,spam detection,retrieval-augmented generation (RAG),LLM-powered,large language models,machine learning,machine learning systems,NLP,natural language understanding,prompting techniques,prompt engineering,thoughtful,curious,AI-assisted development,benchmarking,prototyping,Data Science,experimentation,ideation,intelligent systems,measurement techniques,product innovation,production systems,evaluation frameworks,scalability,technical strategy,vector databases,workflows