About this role
The Amazon Music Catalog Quality team is seeking an Applied Scientist to develop solutions that ensure and improve the quality of catalog metadata and content. You will work on detecting, measuring, and remediating quality issues in music metadata, including artist information, track attributes, and content tags. You will leverage advanced technologies such as Generative AI, classical ML, NLP, Computer Vision, and automated data validation pipelines to influence the music streaming experience globally. Key Responsibilities: Design and develop end-to-end systems, create technical roadmaps, and drive production-level projects. Collaborate with scientists, engineers, and product managers to frame business problems as ML or optimization tasks. Utilize machine learning, deep learning, LLMs, and Agentic AI to create scalable solutions. Analyze large datasets to automate processes, design and evaluate predictive models, and implement scalable data pipelines and model-serving systems. Communicate findings to technical and non-technical stakeholders. Basic Qualifications: 3+ years of experience building models for business applications. PhD, or Master's degree with 4+ years of experience in CS, CE, ML, or a related field. Proficiency in Java, C++, or Python. Experience in algorithms, data structures, parsing, numerical optimization, data mining, and high-performance computing. Preferred Qualifications: Experience with Unix/Linux environments. PhD in computer science, machine learning, engineering, or related fields.