Music ML Scientist: Personalize & Predict at Scale

Amazon Music; Sunnyvale (CA); On-site; USD 172,000 - 222,000; Full time

Job summary

Amazon Music is seeking an experienced Applied Scientist to advance machine learning for music understanding, classification, and interactive experiences. You will deploy scalable ML models and collaborate with software engineers to broaden impact across Alexa, mobile, and web platforms.

You will work with large datasets, publish findings, and contribute to state-of-the-art approaches in music information retrieval, NLP, and recommender systems within a collaborative team environment.

Qualifications

• PhD or MS with 4+ years in CS/CE/ML or related field.
• 3+ years building ML models for business applications.
• Experience programming in Java, C++, Python or related language.
• Experience in algorithms, data structures, numerical optimization, data mining, parallel and distributed computing.

Responsibilities

• Use machine learning, deep learning, LLMs and Agentic AI techniques to create scalable solutions for business problems.
• Analyze and extract relevant information from large amounts of data to automate and optimize key processes.
• Design, development and evaluation of AI models for predictive learning.
• Work closely with software engineering teams to drive model implementations and new feature creations.
• Establish scalable, efficient automated processes for large scale data analyses, model development, validation and implementation.

Skills

Machine learning

Java

C++

Python

Algorithms

Education

PhD

Master's in CS/CE/ML

Tools

Java

C++

Python

Job description

Amazon Music is seeking an experienced Applied Scientist to advance machine learning for music understanding, classification, and interactive experiences. You will deploy scalable ML models and collaborate with software engineers to broaden impact across Alexa, mobile, and web platforms.

You will work with large datasets, publish findings, and contribute to state-of-the-art approaches in music information retrieval, NLP, and recommender systems within a collaborative team environment.