Applied Scientist, Music - Catalog MusicIQ

Description

Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert live streams 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.

• 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.

• Advocate solutions and communicate results, insights and recommendations to stakeholders and partners.

• Produce innovative research on catalog, music content understanding, ranking, sequencing and recommendation 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.

Lead innovation in AI/ML to shape Amazon Music experiences for millions. Develop state of the art models leveraging and advancing the latest developments in machine learning and genAI. 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 ovate daily alongside world‑class teams to delight customers worldwide through personalization.

• 3+ years of building models for business application experience

• PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience

• Experience programming in Java, C++, Python or related language

• Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

• Experience building machine learning models or developing algorithms for business application

• Experience in CS, CE, ML or related field research

• Experience in designing experiments and statistical analysis of results

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, 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 (Use the "Apply for this Job" box below). for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $136,000/year in our lowest geographic market up to $223,400/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  This position will remain posted until filled. Applicants should apply via our internal or external career site.