Machine Learning Engineer, Amazon Music - Catalog Quality
The Music Catalog Quality team is seeking a passionate and talented Machine Learning Engineer (MLE) to join us. The Music Catalog Quality team is on a mission to enable accurate, complete, and enriched music metadata and content across the Amazon Music catalog. Our team is responsible for detecting, correcting, and enriching catalog metadata in real-time, leveraging technologies such as large language models (LLMs), computer vision, natural language processing, deep learning classifiers, and related methods.
Our mission is to provide high-quality, dynamically validated and enriched catalog metadata with low latencies across the Amazon Music experience. We automate the detection and correction of metadata anomalies, including misattributed tracks, duplicate content, incorrect artist information, and incomplete album details, while providing internal teams with the tools and flexibility to continuously improve catalog integrity at scale.
Key job responsibilities
- Design, build, and operate scalable machine learning pipelines and online serving systems
- Work closely with applied scientists to optimize ML model performance and implement end-to-end solutions from experimentation through production
- Drive technology choices and continuous innovation for ML infrastructure across the sponsored products organization
- Collaborate with product managers, scientists, and engineers to deliver the right product for customers
- Build and maintain strong relationships across partner disciplines (Product, Science and Engg) to ensure customer-focused delivery
- Contribute to operational excellence - monitoring, troubleshooting, and supporting high-volume, low-latency systems
Our mission is to provide high-quality, dynamically validated and enriched catalog metadata with low latencies across the Amazon Music experience. We automate the detection and correction of metadata anomalies, including misattributed tracks, duplicate content, incorrect artist information, and incomplete album details, while providing internal teams with the tools and flexibility to continuously improve catalog integrity at scale.
Key job responsibilities
- Design, build, and operate scalable machine learning pipelines and online serving systems
- Work closely with applied scientists to optimize ML model performance and implement end-to-end solutions from experimentation through production
- Drive technology choices and continuous innovation for ML infrastructure across the sponsored products organization
- Collaborate with product managers, scientists, and engineers to deliver the right product for customers
- Build and maintain strong relationships across partner disciplines (Product, Science and Engg) to ensure customer-focused delivery
- Contribute to operational excellence - monitoring, troubleshooting, and supporting high-volume, low-latency systems