Sr. Software Dev Engineer, Music Catalog

Sr. Software Dev Engineer, Amazon Music Catalog

Job :  |  Services LLC

We are seeking a Senior Software Development Engineer (Sr. SDE) to join the Music Catalog team and drive the design and evolution of scalable, high‑performance catalog 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 metadata ingestion, 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.

• Lead the technical direction for major features and products within the developer experience portfolio.
• Drive technical decisions and establish best practices for your team’s codebase and infrastructure.
• Mentor SDE 1s and SDE 2s, conducting design reviews and providing technical guidance.
• Partner with Principal Engineers to solve complex technical challenges and align on architectural approaches.
• Create automation solutions that measurably reduce toil and improve developer workflows.
• Design APIs and frameworks that other teams can leverage to build their own productivity solutions.
• Ensure your solutions are scalable, maintainable, and operationally excellent.
• Develop, deploy and scale robust GenAI and ML solutions in production environment.
• Own building models for production and feed AI/ML outputs to systems and services by partnering closely with Applied Scientists, SDEs, Product Managers, and Technical Program Managers.
• Partner with teams across the organization to deeply understand their pain points and requirements.
• Work with Principal Engineers to validate technical approaches and incorporate feedback.
• Collaborate with your manager and other senior engineers to define roadmaps and prioritize initiatives.
• Influence technical decisions across partner teams.
• Present technical designs and results to leadership and stakeholders.

The Amazon Music Catalog team is responsible for persisting, computing, reconciling and vending Music rights and metadata to other teams and services across Amazon. We process hundreds of millions of updates per day and our services serve tens of thousands of requests per second to all Amazon Music customers. We own critical platforms that make access to the Amazon Music Catalog highly available and accessible to everyone.

We are solving world class problems that haven’t been tackled before!

Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert live streams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service:
Prime members get access to all the music in shuffle mode, and top ad‑free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on‑demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa‑enabled devices. Learn more at

• 5+ years of non‑internship professional software development experience.
• 5+ years of programming with at least one software programming language experience.
• 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems.
• Experience as a mentor, tech lead or leading an engineering team.
• Experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware.
• Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference life cycles, and optimization of…