Senior Music ML Scientist – Recommender Systems

Amazon Music; Sunnyvale (CA); On-site; USD 192,000 - 260,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Job summary

Amazon Music is seeking an Applied Scientist to join the Search Science team in Sunnyvale, CA. You will work on machine learning problems for music classification, recommender systems, dialogue systems, NLP, and music information retrieval, deploying scalable models that impact Alexa, mobile, and web experiences.

You will collaborate with software engineers, researchers, and product teams to translate insights into impactful features, publish findings, and push the boundaries of AI-driven music

Qualifications

• PhD or Master’s with 6+ years of applied research.
• 3+ years building ML models for business applications.
• Experience with Java, C++, Python.

Responsibilities

• Use ML, deep learning, LLMs and Agentic AI to create scalable solutions for business problems.
• Analyze and extract relevant information from large data to help 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, model validation and model implementation.
• Research and implement novel machine learning and statistical approaches.

Skills

Deep learning

LLMs

Agentic AI

Statistical methods

Education

PhD

Master's degree

Tools

Java

C++

Python

R

scikit-learn

Spark MLLib

MxNet

TensorFlow

NumPy

SciPy

Job description

Amazon Music is seeking an Applied Scientist to join the Search Science team in Sunnyvale, CA. You will work on machine learning problems for music classification, recommender systems, dialogue systems, NLP, and music information retrieval, deploying scalable models that impact Alexa, mobile, and web experiences.

You will collaborate with software engineers, researchers, and product teams to translate insights into impactful features, publish findings, and push the boundaries of AI-driven music