Machine Learning Engineer, Catalog Quality

- 3+ 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 working with PyTorch or JAX software
- 2+ years of building large-scale machine-learning infrastructure for online recommendation, ads ranking, personalization or search experience
- 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Master's degree in computer science or equivalent
- Knowledge of Machine Learning and LLM fundamentals, including transformer architecture, training/inference lifecycles, and optimization techniques

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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