Machine Learning Engineer, Amazon Music Catalog [MusicIQ]

- 2+ 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 programming with at least one software programming language
- Knowledge of professional software engineering & best practices for full software development life cycle, including coding standards, software architectures, code reviews, source control management, continuous deployments, testing, and operational excellence
- 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Bachelor's degree in computer science or equivalent
- Experience in ML Ops using modern cloud solutions like Sagemaker, Kubernetes, Airflow etc.
- Proficiency in Python/Kotlin.
- Experience with AutoML platforms.

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.

Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $129,300/year in our lowest geographic market up to $223,600/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.
Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams 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. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale.

The ideal candidate is interested in being part of a growing team that is passionate about experimenting and innovating for customers, has a demonstrable track record of success in delivering new features and products, and is excited about having end-to-end ownership of high-impact, high-visibility projects. A commitment to collaboration, proficiency in evaluating alternative solutions, and strong communication skills (with both business and technical partners) are absolute requirements. The role requires having a solid depth of technical knowledge, being well-versed in industry best practices, and possessing attention to details.

Key job responsibilities
Key responsibilities are enhancing core ML Infrastructure for tagging music content and helping improve Music similarity. You will work closely with Research scientists to deploy scalable ML models to production, improving model performance and architecture.

A day in the life
Investigate design approaches, prototype new technologies, and evaluate their technical feasibility, such as Auto ML, 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/services for developing, evaluating, and deploying machine learning models used in real-world applications.

About the team
Our team is responsible for enhancing the quality and breadth of metadata within the Amazon Music Catalog. Our primary focus is on improving the accuracy and completeness of existing metadata while also enriching it to support advanced voice and visual experiences. We own refinement, augmentation and generation of specialized metadata such as Genre, Era, Mood, and Activity. Additionally, the team is responsible for improving overall "Music IQ" by producing sophisticated knowledge features like semantic audio characteristics and GenAI based audio, thematic, and cultural embeddings to improve Music similarity.