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

What the job involves

• 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
• 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

Benefits

• Extensive learning opportunities, through our dedicated team, GreenHouse
• Flexible share incentives letting you choose how you share in our success
• Global parental leave, six months off - fully paid - for all new parents
• All The Feels, our employee assistance program and self-care hub
• Flexible public holidays, swap days off according to your values and beliefs