Full Time Job

Manager Applied Science, Machine Learning / Personalization

Amazon Music

Berlin, Germany 07-01-2024
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  • Paid
  • Full Time
Job 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 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.

You will be managing a cross-functional team of Applied Scientists and (Machine Learning) Engineers, driving innovation in ML/AI models for customer and content understanding that drive key personalization tasks like recommendation, ranking, and experience optimization. You will lead your team in setting up roadmaps, experimentation and development, all the way to bringing new products out to improve the Amazon Music customer experience.

Key job responsibilities
- Lead a group of Applied Scientists, ML Engineers and Software Development Engineers located in Berlin, Germany to deliver innovation and solutions-in-production that utilize ML/AI to solve business and customer problems
- Advance the team's craftsmanship and drive continued scientific innovation as a thought leader and practitioner, also influencing beyond your own team into the entire Personalization org
- Develop the roadmap for your team, in collaboration with other teams in the Personalization org and Music at large; oversee planning, and foster cross-team collaboration to execute on complex projects
- Hire and develop top talent, provide technical and career development guidance to people across the organization
- Leverage industry best practices to establish repeatable applied science practices, principles & processes
- Evangelize state-of-the-art and innovation to the wider Amazon Music org

About the team
The Music Machine Learning & Personalization team in Berlin is responsible for foundational ML/AI infrastructure in Amazon Music; providing ML artifacts that capture customer- and content insights; and key primitives for delivering a personalized customer experience, including ranking, experience optimization. The team works closely with other teams in the US (e.g. Seattle, San Francisco), and has a very vibrant, diverse, international, and multi-cultural atmosphere - like Berlin!

- PhD, or Master's degree in Computer Science, Machine Learning, Mathematics or related field
-Applied research experience
- Experience in leading scientists or machine learning engineers
- Experience in building machine learning models or developing algorithms for business applications
- Experience programming in Python or related language

- Experience building complex software systems, especially involving deep learning, machine learning, that have been successfully delivered to customers
- Experience in patents or publications at top-tier peer-reviewed conferences or journals, strong publication record
- Experience with popular deep learning frameworks, including PyTorch
- Experience with learning LLMs and Gen AI, both textual and multi-modal approaches

Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice ( to know more about how we collect, use and transfer the personal data of our candidates.


Jobcode: Reference SBJ-gq0439-3-92-91-54-42 in your application.