AmazonPosted 1mo ago

Machine Learning Engineer, Amazon Music - Catalog Quality

Machine Learning Engineer, Amazon Music - Catalog Quality at Amazon scores 97 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.

IN, KA, Bengalurumidfull-time

AI in this role

pytorchjax

computer-visionnlp

The Music Catalog Quality team is seeking a passionate and talented Machine Learning Engineer (MLE) to join us. The Music Catalog Quality team is on a mission to enable accurate, complete, and enriched music metadata and content across the Amazon Music catalog. Our team is responsible for detecting, correcting, and enriching catalog metadata in real-time, leveraging technologies such as large language models (LLMs), computer vision, natural language processing, deep learning classifiers, and related methods.

Our mission is to provide high-quality, dynamically validated and enriched catalog metadata with low latencies across the Amazon Music experience. We automate the detection and correction of metadata anomalies, including misattributed tracks, duplicate content, incorrect artist information, and incomplete album details, while providing internal teams with the tools and flexibility to continuously improve catalog integrity at scale.

Key job responsibilities
- Design, build, and operate scalable machine learning pipelines and online serving systems
- Work closely with applied scientists to optimize ML model performance and implement end-to-end solutions from experimentation through production
- Drive technology choices and continuous innovation for ML infrastructure across the sponsored products organization
- Collaborate with product managers, scientists, and engineers to deliver the right product for customers
- Build and maintain strong relationships across partner disciplines (Product, Science and Engg) to ensure customer-focused delivery
- Contribute to operational excellence - monitoring, troubleshooting, and supporting high-volume, low-latency systems

Basic qualifications

- 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

Preferred qualifications

- 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

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

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A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Computer VisionNlpPyTorchJax

Questions you could be asked

Walk me through a computer vision problem you solved, from raw data to a deployed model.
What NLP problem have you worked on, and how did you measure whether it actually worked?
What are the limits of PyTorch that you've run into, and how did you work around them?
What's a project where you used Jax hands-on?
How would you decide a model or AI system is ready to ship?

Adapt your resume

List these exact terms on your resume: Computer Vision, Nlp, PyTorch, and Jax. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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