Machine Learning Engineer II, Amazon Music - MusicIQ
Posted Updated
We are seeking a Machine Learning Engineer to join the MusicIQ team and drive the design and evolution of scalable, high-performance machine learning systems. In this role, you will work at the intersection of large-scale distributed systems, data pipelines, and applied ML/AI, building platforms that power content intelligence, and catalog serving at Amazon scale.
You will partner closely with Applied Scientists, Product Managers, and partner engineering teams to deliver systems that are reliable, cost-efficient, and optimized for both offline processing and real-time customer experiences. Your work will focus on improving system scalability, data quality, and operational efficiency, while enabling faster iteration and integration of AI-driven capabilities across the catalog ecosystem.
Key job responsibilities
- Enhance core ML infrastructure for tagging music content and improve music similarity capabilities.
- Partner with research scientists to deploy scalable ML models to production, improving model performance and architecture.
- Investigate design approaches, prototype new technologies, and evaluate their technical feasibility (e.g., AutoML, real-time ML serving systems).
- Collaborate with scientists to design and build data pipelines for processing massive datasets and scaling machine learning models.
- Develop and maintain platforms and services for building, evaluating, and deploying machine learning models used in real-world applications.
You will partner closely with Applied Scientists, Product Managers, and partner engineering teams to deliver systems that are reliable, cost-efficient, and optimized for both offline processing and real-time customer experiences. Your work will focus on improving system scalability, data quality, and operational efficiency, while enabling faster iteration and integration of AI-driven capabilities across the catalog ecosystem.
Key job responsibilities
- Enhance core ML infrastructure for tagging music content and improve music similarity capabilities.
- Partner with research scientists to deploy scalable ML models to production, improving model performance and architecture.
- Investigate design approaches, prototype new technologies, and evaluate their technical feasibility (e.g., AutoML, real-time ML serving systems).
- Collaborate with scientists to design and build data pipelines for processing massive datasets and scaling machine learning models.
- Develop and maintain platforms and services for building, evaluating, and deploying machine learning models used in real-world applications.