Software Development Engineer, Amazon Music Catalog [MusicIQ]

Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture.

The ideal candidate for this Software Development Engineer role in the Amazon Music Catalog [MusicIQ] team is interested in being part of a growing team that is passionate about experimenting and innovating for customers. They should have a demonstrable track record of success in delivering new features and products, and be excited about having end-to-end ownership of high-impact, high-visibility projects.

Key responsibilities include:

• Enhancing core ML Infrastructure for tagging music content
• Improving Music similarity
• Working closely with Research scientists to deploy scalable ML models to production
• Improving model performance and architecture
• Investigating design approaches, prototyping new technologies, and evaluating their technical feasibility
• Collaborating with scientists to design and build data pipelines for processing massive datasets
• Developing and maintaining platforms/services for developing, evaluating, and deploying machine learning models

The team is responsible for enhancing the quality and breadth of metadata within Amazon Music Catalog, focusing on improving accuracy, completeness, and enrichment to support advanced voice and visual experiences. They own refinement, augmentation, and generation of specialized metadata such as Genre, Era, Mood, and Activity. Additionally, the team is responsible for improving overall "Music IQ" by producing sophisticated knowledge features like semantic audio characteristics and GenAI based audio, thematic, and cultural embeddings to improve Music similarity.

This role offers the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale, working at the intersection of music, technology, and machine learning.