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About the Role
The Experience team designs Spotify’s consumer experience across all platforms and integrations, aiming to make listening effortless, personal, and joyful for billions of users. The Enrichment & Content Intelligence team, within Content Platform, builds metadata-resolution and content-enrichment infrastructure to understand music and video content globally. These systems answer foundational questions about content relationships and power products used by millions of listeners, artists, and creators daily. This role involves evolving machine learning systems for Recording Groups, Music Video Resolution, SongDNA, and the Music Knowledge Graph, focusing on multimodal machine learning, entity resolution, and production-scale engineering for audio, video, and metadata understanding.
Responsibilities
- Own and evolve large-scale ML pipelines for content-resolution systems.
- Lead development of multimodal embedding frameworks for understanding, music video matching, and SongDNA.
- Improve entity-resolution systems across music and video content to better understand relationships between recordings, versions, and content formats.
- Design and run experiments to enhance content quality using offline evaluation, golden datasets, A/B testing, and impact analysis.
- Build scalable ML evaluation and monitoring infrastructure, including standardized datasets, retraining workflows, and continuous improvement systems.
- Contribute to the Music Knowledge Graph's evolution by improving production ML capabilities, observability, and model lifecycle management.
- Partner with Product Managers, Data Scientists, and engineering teams across Content Platform and the Experience Mission.
- Shape technical strategy for the squad and contribute to long-term ML direction.
- Mentor engineers and foster a culture of technical collaboration and experimentation.
Requirements
- Solid experience building, deploying, and maintaining production-scale machine learning systems.
- Strong experience training, evaluating, and operating ML models using PyTorch or TensorFlow.
- Experience with multimodal machine learning systems across audio, computer vision, text embeddings, or related domains.
- Understanding of entity resolution, deduplication, record linkage, or large-scale matching problems, especially across multiple content modalities.
- Ability to design evaluation systems balancing model quality, operational performance, and real-world impact.
- Experience with large-scale distributed data processing systems and ML infrastructure.
- Effective communication across engineering, product, and data science stakeholders.
- Comfort leading technical initiatives and influencing engineering direction within a team.
Skills
- Scio
- Dataflow
- Flyte
- BigQuery
- Scala
- Computer vision
- Video understanding
- Multimodal embeddings
- Recommendation systems
Location
- New York City
Work Type
- Hybrid
Experience Level
- Senior level
Salary/Compensations
- $184,049–262,928 USD, plus equity
Benefits
- Health insurance
- Six-month paid parental leave
- 401(k) retirement plan
- Monthly meal allowance
- 23 paid days off
- Paid flexible holidays
- Paid sick leave