Machine Learning Engineer, Amazon Music Catalog
1 day ago
Job ID: 2826694 | Amazon.com Services LLC
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. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode, and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale.
The ideal candidate is interested in being part of a growing team that is passionate about experimenting and innovating for customers, has a demonstrable track record of success in delivering new features and products, and is excited about having end-to-end ownership of high-impact, high-visibility projects. A commitment to collaboration, proficiency in evaluating alternative solutions, and strong communication skills (with both business and technical partners) are absolute requirements. The role requires having a solid depth of technical knowledge, being well-versed in industry best practices, and possessing attention to details.
Key Responsibilities- Enhancing core ML Infrastructure for tagging music content and helping improve 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, such as Auto ML, real-time ML serving systems.
- Collaborating with scientists to design and build data pipelines for processing massive datasets and scaling machine learning models.
- Developing and maintaining platforms/services for developing, evaluating, and deploying machine learning models used in real-world applications.
Our team is responsible for enhancing the quality and breadth of metadata within the Amazon Music Catalog. Our primary focus is on improving the accuracy and completeness of existing metadata while also enriching it to support advanced voice and visual experiences. We 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.
BASIC QUALIFICATIONS- 2+ years of non-internship professional software development experience.
- 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience.
- Experience programming with at least one software programming language.
- Knowledge of professional software engineering & best practices for full software development life cycle, including coding standards, software architectures, code reviews, source control management, continuous deployments, testing, and operational excellence.
- 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience.
- Bachelor's degree in computer science or equivalent.
- Experience in ML Ops using modern cloud solutions like Sagemaker, Kubernetes, Airflow etc.
- Proficiency in Python/Kotlin.
- Experience with AutoML platforms.
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
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