Job Details

Applied Scientist, Amazon Music Machine Learning/Personalization

Job ID: 2884821 | Amazon.com Services LLC

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.

Key job responsibilities

1. Work backwards from customer problems to research and develop novel machine learning solutions for music and podcast recommendations. Through A/B testing and online experiments done hand-in-hand with engineering teams, you'll implement and validate your ideas and solutions.
2. Advocate solutions and communicate results, insights and recommendations to stakeholders and partners.
3. Produce innovative research on recommender systems that shapes the field and meets the high standards of peer-reviewed publications. You'll cement your team's reputation as thought leaders pioneering new recommenders. Stay current with advancements in the field, adapting latest in literature to build efficient and scalable models.

A day in the life

Lead innovation in ML to shape Amazon Music experiences for millions. Collaborate with talented engineers and scientists to guide research and build scalable models across our audio portfolio - music, podcasts, live streaming, and more. Drive experiments and rapid prototyping, leveraging Amazon's data at scale. Innovate daily alongside world-class teams to delight customers worldwide through personalization.

About the team

The team is responsible for models that underlie all of Amazon Music's recommendations across content types on mobile, web and Alexa. You will collaborate with a team of product managers, applied scientists and software engineers delivering meaningful recommendations, personalized for each of the millions of customers using Amazon Music globally. As a scientist on the team, you will be involved in every aspect of the development lifecycle, from idea generation and scientific research to development and deployment of advanced models. You will work closely with engineering to realize your scientific vision.

BASIC QUALIFICATIONS

1. 3+ years of building models for business application experience
2. PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
3. Experience programming in Java, C++, Python or related language
4. Experience in patents or publications at top-tier peer-reviewed conferences or journals

PREFERRED QUALIFICATIONS

1. Experience implementing algorithms using toolkits and self-developed code
2. Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
3. Have knowledge and practical experience in several of the following areas: machine learning, statistics, deep learning, LLMs, NLP, recommendation systems, information retrieval;

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.

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 here for more information.

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