Machine Learning Engineer, Amazon Music - Catalog Quality

Amazon Amazon · Big Tech · IN, KA, Bengaluru · Software Development

Machine Learning Engineer at Amazon Music focused on catalog quality, leveraging LLMs, computer vision, and deep learning to detect, correct, and enrich music metadata in real-time. The role involves designing, building, and operating scalable ML pipelines and online serving systems, optimizing model performance, and contributing to ML infrastructure.

What you'd actually do

  1. Design, build, and operate scalable machine learning pipelines and online serving systems
  2. Work closely with applied scientists to optimize ML model performance and implement end-to-end solutions from experimentation through production
  3. Drive technology choices and continuous innovation for ML infrastructure across the sponsored products organization
  4. Collaborate with product managers, scientists, and engineers to deliver the right product for customers
  5. Build and maintain strong relationships across partner disciplines (Product, Science and Engg) to ensure customer-focused delivery

Skills

Required

  • 3+ years of non-internship professional software development experience
  • 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
  • Experience working with PyTorch or JAX software
  • 2+ years of building large-scale machine-learning infrastructure for online recommendation, ads ranking, personalization or search experience

Nice to have

  • 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
  • Master's degree in computer science or equivalent
  • Knowledge of Machine Learning and LLM fundamentals, including transformer architecture, training/inference lifecycles, and optimization techniques

What the JD emphasized

  • large language models (LLMs)
  • low latencies
  • high-volume, low-latency systems
  • online recommendation, ads ranking, personalization or search experience

Other signals

  • ML pipelines
  • online serving systems
  • ML model performance
  • end-to-end solutions
  • ML infrastructure
  • high-volume, low-latency systems