About the Role
Role details below.
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
Requirements
- 5+ years of experience in machine learning, data, or backend engineering
- Experience with production-grade systems and scalable architectures
- Worked on recommendation systems, ranking, or optimization problems
- T-shaped skillset across ML, data, and backend domains
- Comfortable navigating ambiguity and solving complex problems
- Care about user experience and measurable impact
- Enjoy collaborating across disciplines and geographies
Benefits
- Flexibility to work where you work best
- Some in-person meetings, but allows for flexibility to work from home
- Equal opportunity employer: welcoming people of all backgrounds, identities, and experiences
- Commitment to inclusivity and accessibility in recruitment
- Reasonable accommodations available during the interview process
- Support available at any stage of the application or interview process
Work Arrangement
Hybrid
Additional Information
- Spotify is an equal opportunity employer and welcomes applicants regardless of background, identity, or appearance.
- The company is committed to inclusivity and making the recruitment process accessible to everyone.
- Applicants can request reasonable accommodations during the interview process.
- The role involves collaboration across disciplines and geographies.
- The team delivers real-time personalization at scale.
