Amazon Hiring Machine Learning Engineer in India | Music Catalog

Job Overview

Amazon is hiring a Machine Learning Engineer for the Amazon Music Catalog Quality team in India. This role focuses on building machine learning systems that help improve the accuracy, completeness, and enrichment of music metadata at large scale. The team works on challenging catalog-quality problems such as identifying incorrect artist information, duplicate content, misattributed tracks, and incomplete album details.

As a Machine Learning Engineer, you will help take machine learning solutions from experimentation into production. The work combines software engineering, machine learning infrastructure, online serving, model optimization, and operational support. You will collaborate with applied scientists, product managers, and other engineers to build reliable systems that can process catalog information with low latency.

The role is particularly relevant for experienced machine learning and software engineers who enjoy solving large-scale engineering problems and building production systems around modern ML technologies.

Key Responsibilities

• Design, develop, and operate scalable machine learning pipelines and online serving systems.
• Work with applied scientists to improve model performance and turn experimental approaches into production-ready solutions.
• Contribute to end-to-end machine learning implementation, from experimentation and system design through deployment and ongoing operation.
• Help make technology decisions for machine learning infrastructure and contribute ideas that improve scalability, reliability, and engineering efficiency.
• Collaborate with product managers, scientists, software engineers, and partner teams to translate customer and business needs into practical technical solutions.
• Build productive relationships across Product, Science, and Engineering teams to support effective delivery.
• Monitor production systems, investigate operational issues, troubleshoot failures, and contribute to the reliability of high-volume, low-latency services.
• Participate in engineering practices that support maintainable and scalable machine learning platforms.

Required Skills

Candidates should have at least three years of professional, non-internship software development experience. The role also requires at least two years of experience designing or architecting new and existing systems, including knowledge of design patterns, reliability, and scaling.

Hands-on experience with PyTorch or JAX is required. Candidates should also have at least two years of experience building large-scale machine learning infrastructure for online recommendation, advertising ranking, personalization, or search-related experiences.

A strong foundation in software engineering is important because this position involves production infrastructure rather than only model experimentation. Applicants should be comfortable thinking about system reliability, scalability, performance, maintainability, and operational support.

Preferred Skills

Experience across the complete software development lifecycle is preferred, including coding standards, code reviews, source control management, build processes, testing, and production operations.

Knowledge of machine learning and large language model fundamentals is also valuable. Relevant areas include transformer architecture, model training and inference lifecycles, and optimization techniques.

A Master's degree in Computer Science or an equivalent qualification is preferred but is not stated as mandatory in the provided requirements.

Education

The provided qualifications indicate that a Master's degree in Computer Science or equivalent is preferred. The basic qualifications do not specify a mandatory degree requirement, so applicants should focus on demonstrating the required professional software development, system architecture, and machine learning infrastructure experience.

Experience

A minimum of three years of non-internship professional software development experience is required. In addition, candidates need at least two years of experience with system design or architecture involving reliability and scaling.

The role also requires at least two years of experience building large-scale machine learning infrastructure for online recommendation, advertising ranking, personalization, or search experiences. These requirements make the position suitable for experienced machine learning engineers and software engineers who have worked on production-scale ML platforms.

Required Technologies

The key technology requirement is practical experience with PyTorch or JAX. The role also involves machine learning infrastructure, online serving systems, scalable software systems, and production ML pipelines.

The team works with modern machine learning approaches including large language models, natural language processing, computer vision, and deep learning classifiers. The job description specifically identifies these as technologies used by the broader Catalog Quality team.

Soft Skills

Strong collaboration is important because the engineer will work closely with applied scientists, product managers, software engineers, and partner teams. Candidates should be able to communicate technical decisions clearly and work effectively across different disciplines.

Problem-solving and ownership are also important. The role involves troubleshooting production systems, improving operational excellence, and contributing to technology decisions. Engineers should be comfortable handling complex technical challenges while keeping reliability and customer-focused delivery in mind.

Benefits of Working in this Role

This role provides an opportunity to work on machine learning systems connected to a large-scale music catalog and customer experience. It combines software engineering with machine learning infrastructure, online serving, system architecture, and operational excellence.

Professionals can deepen their experience with production ML systems, scalable infrastructure, model-serving workflows, and collaboration between science and engineering teams. The work also provides exposure to modern areas such as LLMs, natural language processing, computer vision, and deep learning.

Work Mode

The provided job description does not specify whether this position is onsite, hybrid, or remote. The job is listed for India.

Location

The provided job description identifies Amazon Development Centre (India) Private Limited and does not specify a particular Indian city. Therefore, the location is recorded as India rather than inventing a city.

Who Should Apply

This opportunity is designed for experienced Machine Learning Engineers and software engineers with production-scale machine learning infrastructure experience. Candidates should have strong software development skills, practical PyTorch or JAX experience, and a background in systems that support recommendation, advertising ranking, personalization, or search.

Applicants who enjoy combining machine learning with scalable software engineering may find this role a strong match. Experience moving ML solutions from experimentation into reliable production services is especially relevant.

Career Growth

Working in this position can strengthen expertise in machine learning infrastructure, online serving, system architecture, distributed-scale engineering, model optimization, and production operations. The experience can support future opportunities in machine learning engineering, ML platform engineering, software architecture, applied machine learning infrastructure, or technical leadership.

Application Advice

Tailor your resume to clearly show professional software development experience and your contribution to large-scale machine learning systems. Highlight PyTorch or JAX work, system architecture responsibilities, reliability and scaling experience, and production ML infrastructure projects.

If you have worked on recommendation systems, advertising ranking, personalization, or search experiences, describe the scale and engineering impact of that work. Also mention experience with code reviews, source control, testing, deployment, operations, model training or inference, and optimization where applicable.