Spotify - Machine Learning Engineer, Personalization
Upload My Resume
Drop here or click to browse · PDF, DOCX, DOC, RTF, TXT
Requirements
• You have 5+ years of experience in machine learning, data, or backend engineering • You are experienced with production-grade systems and scalable architectures • You have worked on recommendation systems, ranking, or optimization problems • You bring a T-shaped skillset across ML, data, and backend domains • You are comfortable navigating ambiguity and solving complex problems • You care about user experience and measurable impact • You enjoy collaborating across disciplines and geographies • 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 $148,901- $212,716 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. • 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.
Responsibilities
• Design and build machine learning systems that optimize ranking and sequencing across personalized surfaces • Develop multi-objective optimization strategies that balance user satisfaction with business outcomes • Collaborate closely with cross-functional partners including product, data science, and engineering teams to align on goals, share context, and deliver impactful solutions • Work across ML, backend, and data layers to bring models into production • Contribute to scalable infrastructure supporting high-volume user interactions • Run experiments and use insights to continuously improve performance • Help shape technical direction and raise the bar for engineering excellence within the team
No credit card. Takes 10 seconds.