Help us deliver meaningful recommendations, personalized for each of the millions of customer engaging with Amazon Music.
The Music Personalization team is responsible for the machine learning models that underly Amazon Music’s recommendations, playlists and stations. We own the development, training and serving of these models. We create personalized recommendation experiences such as You Might Like recommender, New Releases for You recommender and My Discovery Mix playlist.
As an Applied Scientist, your work will have a real world impact on the millions of customers using Amazon Music. You will start with formulating product requirements into a scientific problem. You will work closely with engineering to realize your scientific vision.
About Amazon Music
Imagine being a part of an agile team where your ideas have the potential to reach millions. Picture working on cutting-edge consumer-facing products, where every single team member is a critical voice in the decision-making process. Envision being able to leverage the resources of a Fortune-500 company within the atmosphere of a start-up. Welcome to Amazon Music, where ideas are born and come to life as Amazon Music Unlimited, Prime Music, and so much more.
Everyone on our team has a meaningful impact on product features, new directions in music streaming, and customer engagement. We are looking for new team members across a variety of job functions including software engineering/development, marketing, design, ops and more. Come join us as we make history by launching exciting new projects in the coming year.
Our team is focused on building a personalized, curated, and seamless music experience. We want to help our customers discover up-and-coming artists, while also having access to their favorite established musicians. We build systems that are distributed on a large scale, spanning our music apps, web player, and voice-forward audio engagement on mobile and Amazon Echo devices, powered by Alexa to support our customer base. Amazon Music offerings are available in countries around the world, and our applications support our mission of delivering music to customers in new and exciting ways that enhance their day-to-day lives.
Come innovate with the Amazon Music team!
· PhD or equivalent Master's Degree plus 4+ years of experience in CS, CE, ML or related field
· 2+ years of experience of building machine learning models for business application
· Experience programming in Java, C++, Python or related language
· PhD in Computer Science (Machine Learning, AI, Statistics, or equivalent);
· 2+ years of practical experience applying ML to solve complex problems;
· Knowledge of scientific programming in scripting languages like Python & R
· Ability to distill informal customer requirements into problem definitions, dealing with ambiguity and competing objectives;
· Extensive knowledge and practical experience in several of the following areas: machine learning, statistics, deep learning, NLP, recommendation systems, dialogue systems, information retrieval;
· Track record of scientific publications in premier journals and conferences;
· Strong problem solving skills;
· Experience handling gigabyte and terabyte size datasets;
· Skilled with Java, C++, or other programming language, as well as with R, MATLAB, Python or similar scripting language;
· Professional experience in software development (software design and development life cycle);
· Superior verbal and written communication skills, ability to convey rigorous mathematical concepts and considerations to non-experts.
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.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records