The Home team sits at the heart of the Spotify experience. Home is where hundreds of millions of listeners discover what to play next, re-engage with what they love, and build habits that keep them listening every day. Our mission is to deliver the most compelling, intuitive, and dynamic personalized Home experience in the world, across music, podcasts, audiobooks, and emerging formats.

Generative AI, large-scale recommender systems, and real-time personalization are fundamentally reshaping how Home is built and experienced. From generative candidate generation and ranking to adaptive layouts, page composition, and real-time personalization, Home is where Spotify’s most advanced ML capabilities meet direct user impact.

We are looking for a hands-on Senior Staff Machine Learning Engineer to provide technical leadership for machine learning systems powering Spotify Home. This is a highly impactful individual contributor role focused on shaping the ML strategy, architecture, and execution for Home, while working closely with product, design, data science, and engineering partners.

Join us and help define how Spotify feels the moment users open the app.

What You'll Do

Define and drive the machine learning technical strategy for Spotify Home, spanning retrieval, ranking, page composition, layout optimization, and real-time personalization

Work at the intersection of recommender systems, generative models, and user intent understanding to deliver highly adaptive and engaging Home experiences

Lead hands-on development of ML models and systems, from prototyping new ideas to productionizing solutions at global scale

Provide senior-level technical leadership across multiple teams, influencing architecture, modeling choices, and long-term investments

Design, build, evaluate, ship, and iterate on large-scale ML systems that directly power Home for hundreds of millions of users

Partner closely with product, design, and user research to translate UX goals and user needs into robust ML systems

Drive best practices in experimentation, offline/online evaluation, model lifecycle management, and reliability

Help evolve Home toward more contextual, generative, and intent-aware experiences, leveraging transformers, sequence models, and emerging techniques

Collaborate with platform and foundation teams to effectively leverage shared models and infrastructure while tailoring solutions to Home’s unique needs

Mentor senior engineers and influence technical standards through thoughtful reviews, documentation, and architectural guidance

Act as a technical ambassador for Home within Spotify’s broader ML community, staying current with research and industry trends

Who You Are

A Deep background in machine learning and recommender systems, with a strong track record of translating ML innovation into shipped product impact

Extensive experience building and operating large-scale, user-facing ML systems in production

Comfortable working across the full ML stack: data, modeling, evaluation, serving, experimentation, and iteration

Hands-on experience with or strong interest in transformer-based models, sequence modeling, and/or generative approaches in recommender systems

You have production experience with languages such as Python, Java, or Scala; experience with PyTorch, TensorFlow, or JAX is a strong plus

A strong systems thinker who can reason about latency, scalability, trade-offs, and end-to-end architecture

You thrive in ambiguity and can lead high-impact initiatives where the problem and solution evolve over time

You communicate clearly and effectively, influencing across engineering, product, design, and leadership

You care deeply about experimentation, data-driven decision making, and user experience quality

You have a strong bias to action: building prototypes and MVPs, launching systems in production, and defining clear technical narratives to move ideas forward

You take a team-first approach, helping others succeed while raising the technical bar

You have demonstrated success leading complex technical initiatives and shaping strategy through collaboration

Passion for crafting experiences that delight users and keep the world listening

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

Additional Information

The United States base range for this position is $264,641-$378,058 plus 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.

Spotify is an equal opportunity employer. You are welcome at Spotify for who you are, no matter where you come from, what you look like, or what’s playing in your headphones. Our platform is for everyone, and so is our workplace. The more voices we have represented and amplified in our business, the more we will all thrive, contribute, and be forward-thinking! So bring us your personal experience, your perspectives, and your background. It’s in our differences that we will find the power to keep revolutionizing the way the world listens.

At Spotify, we are passionate about inclusivity and making sure our entire recruitment process is accessible to everyone. We have ways to request reasonable accommodations during the interview process and help assist in what you need. If you need accommodations at any stage of the application or interview process, please let us know - we’re here to support you in any way we can.

Spotify transformed music listening forever when we launched in 2008. Our mission is to unlock the potential of human creativity by giving a million creative artists the opportunity to live off their art and billions of fans the chance to enjoy and be passionate about these creators. Everything we do is driven by our love for music and podcasting. Today, we are the world’s most popular audio streaming subscription service.

Senior Staff Machine Learning Engineer - Home

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