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Spotify
Senior Machine Learning Engineer - Enrichment & Content Intelligence
Spotify
New York•Hybrid•Salary•5-8 Yrs•Jun 23
Job description
machine learning engineermultimodal embedding frameworksentity resolutioncontent intelligence
Overview
The Enrichment & Content Intelligence team builds the metadata-resolution and content-enrichment infrastructure that powers how Spotify understands music and video content at global scale. This role focuses on evolving machine learning systems behind Recording Groups, Music Video Resolution, SongDNA, and the Music Knowledge Graph.
Responsibilities
- Own and evolve large-scale ML pipelines powering Spotify’s content-resolution systems
- Lead development of multimodal embedding frameworks supporting multimodal understanding, music video matching, SongDNA
- Improve entity-resolution systems across music and video content, helping Spotify better understand relationships between recordings, versions, and content formats
- Design and run experiments to improve precision, recall, and overall content-quality outcomes 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 evolution of the Music Knowledge Graph by improving production ML capabilities, observability, and model lifecycle management
- Partner closely with Product Managers, Data Scientists, and engineering teams across Content Platform and the wider Experience Mission
- Help shape technical strategy for the squad and contribute to long-term ML direction across the product area
- Mentor engineers and contribute to a strong culture of technical collaboration and experimentation
Qualifications
- Solid experience building, deploying, and maintaining machine learning systems in production at scale
- Strong experience training, evaluating, and operating ML models using modern frameworks such as PyTorch or TensorFlow
- Experience working 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, ideally across multiple content modalities
- Knowledge in designing evaluation systems that balance model quality, operational performance, and real-world impact
- Experience working with large-scale distributed data processing systems and ML infrastructure
- Effective communication across engineering, product, and data science stakeholders
- Comfortable leading technical initiatives and influencing engineering direction within a team
Nice to have
- Experience with Scio, Dataflow, Flyte, BigQuery, or similar distributed processing frameworks is a plus
- Experience with Scala is a plus
- Experience with computer vision, video understanding, multimodal embeddings, or recommendation systems is a strong plus
Education
- Bachelor's degree in Computer Science, Mathematics, Applied Stats, Machine Learning, or a related field
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