Senior Staff Machine Learning Engineer - Home

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