Senior Staff Machine Learning Engineer

The Personalization team makes deciding what to play next on Spotify easier and more enjoyable for every listener. We seek to understand the world of music and podcasts 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 which include destinations like Home and Search, original playlists like Discover Weekly and Daylist, and are at the forefront of new innovations like AI DJ and AI Playlists. Generative AI is transforming Spotify’s product capabilities and technical architecture. Generative recommender systems, agent frameworks, and LLMs present huge opportunities for our products to serve more user needs and use cases and unlock richer understanding of our content and users. This Senior Staff Machine Learning Engineer will focus on recommender systems modeling at the intersection of generative recommenders and foundational understanding of personalization across music and talk content formats. You will work closely with a cross-functional team to define and execute the machine learning technical strategy for the product area, building the next generation of Spotify content and user representations and the technical architecture to support it. You will work as an individual contributor, offering the opportunity to shape the direction of Home loading paradigms, page serving, content filtering and content storage. Join us and you’ll keep millions of users listening to great recommendations every day! 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. What You'll Do Contribute to defining the machine learning technical strategy at the intersection of generative recommenders and foundational user modeling Collaborate with a cross functional agile team spanning user research, design, data science, product management, and engineering to build new product features that connect fans and artists in personalized, meaningful ways Provide expert technical leadership and direction to accelerate development, ensure scalability and push the boundaries of current methods Contribute to designing, building, evaluating, shipping, and refining Spotify’s personalization products by hands-on ML developmentPrototype new modeling approaches and productionize solutions at scale for our hundreds of millions of active users Promote and role-model best practices of ML model development, testing, evaluation, etc., both inside the team as well as throughout the organization Engage with the broader ML community within Spotify and stay current with ML research to inspire and evolve our approaches Partner closely with teams to translate their needs into foundational systems that enable each step of the core content lifecycle. Mentor engineers and influence technical strategy by setting high standards in methodology, reproducibility, and collaboration. Who You Are You have a strong background in machine learning and recommender systems, and you know how to bridge research and end-user impact You have production experience developing large-scale machine learning systems in Java, Scala, Python, or similar languages. Experience with PyTorch, Tensorflow, JAX is a strong plus You have hands-on experience training and operating transformer models in production settings, or a strong interest in doing so You enjoy leading projects from start to finish working closely with your team and peers You are comfortable dealing with ambiguity on high impact projects You’re a strong communicator and systems thinker who can drive alignment and influence across technical and product stakeholders You care about agile software processes, data-driven development, reliability, and disciplined experimentation You stay current on ML trends and are eager to apply emerging ideas to Spotify’s challenges You’re passionate about the opportunity to enrich the listening experience for users around the world You have extensive experience in designing system architectures that include machine learning models as key components in enabling the product experiences. You have a strong bias to action by building MVPs, prototypes and illustrating ideas through concise documents to drive initiatives forward. Team-first approach with developed techniques to ensure teams are happy, motivated, and productive You enjoy leading projects from start to finish working closely with your team and peers. You are comfortable dealing with ambiguity on high impact projects Accountable to senior tech leadership for meeting our product and technology objectives and managing expectations if those are at risk Demonstrated success leading technical initiatives and shaping strategic directions through cross-functional collaboration. Excellent communication skills and stakeholder management abilities; comfortable operating at the intersection of science and engineering 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

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