Machine Learning Engineer II
The Personalization (PZN) team makes deciding what to play next on Spotify easier and more enjoyable for every listener. We seek to understand the world of music, podcasts, and audiobooks better than anyone else so that we can make great recommendations to every individual and keep the world listening. Every day, hundreds of millions of people all over the world use the products we build—from flagship playlists like Discover Weekly and Daily Mix to entry points like Home and Search.
The Next Generation Sessions (NextGen) product area within PZN is reimagining listening beyond the playlist. We invent cutting-edge personalized sessions like AI DJ, AI Playlist, Blend, Jam, and vertical feeds for discovering music and podcasts. Our team of ~100 engineers, scientists, product managers, and domain experts in music, podcasts, and voice collaborate across New York, Boston, London, Stockholm, and remote locations from Eastern US through Central European time zones.
As an MLE II / AI Engineer in NextGen, you’ll be part of a cross-functional team inventing new AI-powered listening experiences. You’ll explore emerging AI capabilities, prototype end-to-end products, and help bring new features from concept to launch to millions of users. This is a fast-paced, high-growth role ideal for someone early in their career who is eager to learn by building, collaborate closely with senior engineers and researchers, and stay at the forefront of AI applied to music and talk content. If you're passionate about the intersection of AI and music and podcasts, obsessed with exploring new AI capabilities, and excited to shape the future of audio, bring your ideas—we’ll invent the next generation of audio together.
What You'll Do
Rapidly prototype, iterate, and ship AI-powered music and podcast recommendation experiences using the latest capabilities of LLMs, agent frameworks, and Spotify’s recommender systems
Continuously explore and evaluate new models, tools, libraries, and product trends—bringing fresh ideas into the team from the frontiers of AI and open-source innovation
Promote and model best practices in AI engineering—including the use of LLMs and agents to power products, advanced evaluation strategies, and AI-assisted coding tools
Leverage AI coding assistants to rapidly prototype full-stack MVPs
Collaborate with cross-functional partners—including design, user research, product, data science, tech research, and engineering—to bring new ideas from concept to launch
Develop a keen eye for software quality, learning to critically evaluate AI-generated code to ensure it meets Spotify's high standards for correctness, performance, and engineering best practices
Grow your skills with access to Spotify’s comprehensive career development resources and encouragement to develop across disciplines
Build a strong foundation and culture in AI engineering by leveraging the state of the art, learning from mentors, pairing with Staff engineers, and shipping code that reaches hundreds of millions of users.
Who You Are
You’re a full-stack AI prototyper: building your own projects with modern AI tools, experimenting fast, and iterating based on feedback
You’re fully plugged into the AI ecosystem—active in online communities, exploring open-source repos, reading papers, trying every new tool you can get your hands on
You think end-to-end, across the stack—from designing user interactions, to wiring up backends, to building models, to deployment considerations—because you want to see ideas come to life
You have a working knowledge of machine learning fundamentals and hands-on experience with Python, Java, or Scala and modern ML frameworks like PyTorch or TensorFlow
You have experience fine-tuning and evaluating LLMsYou have knowledge and/or experience building workflows that integrate LLMs and data into autonomous, multi-step workflows
You have extensive experience crafting prompts to get the results you’re after. You know how to write, improve, and test your prompts to extract value out of LLMs
You understand the importance of critically reviewing AI-generated code for correctness, quality, and alignment with best practices
You may have studied AI formally—or you may have taught yourself through hands-on building and deep curiosity
You thrive in collaborative, interdisciplinary environments and are eager to learn from more experienced engineers
You bring contagious enthusiasm, a creative mindset, and a strong drive to shape the future of AI-powered products
Where You'll Be
We offer you the flexibility to work where you work best! For this role, you can be within the North America region as long as we have a work location. This team operates within the Eastern Standard time zone for collaboration.
The United States base range for this position is $138,250- $197,500plus equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, paid sick leave. These ranges may be modified in the future.
About the job
Apply for this position
Machine Learning Engineer II
The Personalization (PZN) team makes deciding what to play next on Spotify easier and more enjoyable for every listener. We seek to understand the world of music, podcasts, and audiobooks better than anyone else so that we can make great recommendations to every individual and keep the world listening. Every day, hundreds of millions of people all over the world use the products we build—from flagship playlists like Discover Weekly and Daily Mix to entry points like Home and Search.
The Next Generation Sessions (NextGen) product area within PZN is reimagining listening beyond the playlist. We invent cutting-edge personalized sessions like AI DJ, AI Playlist, Blend, Jam, and vertical feeds for discovering music and podcasts. Our team of ~100 engineers, scientists, product managers, and domain experts in music, podcasts, and voice collaborate across New York, Boston, London, Stockholm, and remote locations from Eastern US through Central European time zones.
As an MLE II / AI Engineer in NextGen, you’ll be part of a cross-functional team inventing new AI-powered listening experiences. You’ll explore emerging AI capabilities, prototype end-to-end products, and help bring new features from concept to launch to millions of users. This is a fast-paced, high-growth role ideal for someone early in their career who is eager to learn by building, collaborate closely with senior engineers and researchers, and stay at the forefront of AI applied to music and talk content. If you're passionate about the intersection of AI and music and podcasts, obsessed with exploring new AI capabilities, and excited to shape the future of audio, bring your ideas—we’ll invent the next generation of audio together.
What You'll Do
Rapidly prototype, iterate, and ship AI-powered music and podcast recommendation experiences using the latest capabilities of LLMs, agent frameworks, and Spotify’s recommender systems
Continuously explore and evaluate new models, tools, libraries, and product trends—bringing fresh ideas into the team from the frontiers of AI and open-source innovation
Promote and model best practices in AI engineering—including the use of LLMs and agents to power products, advanced evaluation strategies, and AI-assisted coding tools
Leverage AI coding assistants to rapidly prototype full-stack MVPs
Collaborate with cross-functional partners—including design, user research, product, data science, tech research, and engineering—to bring new ideas from concept to launch
Develop a keen eye for software quality, learning to critically evaluate AI-generated code to ensure it meets Spotify's high standards for correctness, performance, and engineering best practices
Grow your skills with access to Spotify’s comprehensive career development resources and encouragement to develop across disciplines
Build a strong foundation and culture in AI engineering by leveraging the state of the art, learning from mentors, pairing with Staff engineers, and shipping code that reaches hundreds of millions of users.
Who You Are
You’re a full-stack AI prototyper: building your own projects with modern AI tools, experimenting fast, and iterating based on feedback
You’re fully plugged into the AI ecosystem—active in online communities, exploring open-source repos, reading papers, trying every new tool you can get your hands on
You think end-to-end, across the stack—from designing user interactions, to wiring up backends, to building models, to deployment considerations—because you want to see ideas come to life
You have a working knowledge of machine learning fundamentals and hands-on experience with Python, Java, or Scala and modern ML frameworks like PyTorch or TensorFlow
You have experience fine-tuning and evaluating LLMsYou have knowledge and/or experience building workflows that integrate LLMs and data into autonomous, multi-step workflows
You have extensive experience crafting prompts to get the results you’re after. You know how to write, improve, and test your prompts to extract value out of LLMs
You understand the importance of critically reviewing AI-generated code for correctness, quality, and alignment with best practices
You may have studied AI formally—or you may have taught yourself through hands-on building and deep curiosity
You thrive in collaborative, interdisciplinary environments and are eager to learn from more experienced engineers
You bring contagious enthusiasm, a creative mindset, and a strong drive to shape the future of AI-powered products
Where You'll Be
We offer you the flexibility to work where you work best! For this role, you can be within the North America region as long as we have a work location. This team operates within the Eastern Standard time zone for collaboration.
The United States base range for this position is $138,250- $197,500plus equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, paid sick leave. These ranges may be modified in the future.