At Warner Music Group, we’re a global collective of music makers and music lovers, tech innovators and inspired entrepreneurs, game-changing creatives and passionate team members. Here, we know that each talent makes our collective bolder and brighter. 
Technology is one of the most important parts of our business. Whether it’s signing up new artists; ensuring we provide the right data to Spotify, YouTube, and other digital service providers; or helping artists use the latest AI tools and make thoughtful decisions with data-driven insights – technology plays an invaluable role in our success. The engineering team at Warner Music Group makes all of it a reality. 
At Warner Music Group, we are building cutting edge inference systems to better understand the landscape of music consumption toward the goal of providing actionable insights that drive business growth.  We are hiring talented Machine Learning Engineers to collaborate with our Data Science and Data Platform teams in order to build scalable Machine Learning systems that enable rapid iteration, experimentation, and continual improvement in our modeling efforts.  Our ideal candidate will have strong ML and Data Science skills along with experience and a passion for building efficient, robust software.

Responsibilities:

• Develop a strong understanding of the business problems we are trying to solve.
• Together with Data Scientists and engineering and product leadership, identify and define the ML systems we want to build - and then design, implement, and launch those systems.
• Contribute to model building, evaluation, and launch.
• Ensure that we are able to run modeling experiments and launches with maximal efficiency, quality, reliability, and repeatability in a large-scale environment with > 2TB of incoming data per day and a total corpus in excess of 20PB.
• Mentor more junior MLEs and Data Scientists.
• Work closely with cross-functional partners to define project objectives and deliverables.

Requirements:

• 7+ years of full-time hands-on experience building scaled ML systems, or equivalent.
• Excellent coding and system design skills.  Strong practical ML knowledge, working knowledge of ML theory and how it influences modeling choices.
• High sense of ownership and a drive to deliver impact in a fast-paced, evolving, ambiguous environment
• Ability to collaborate closely with Data Scientists, Software Engineers, and Product Manager.
• Strong communication skills and ability to influence roadmaps and ML strategy.
• Experience with cloud computing services or platforms (preferably AWS)
• Experience with both Snowflake and Databricks is a plus
• Bachelor’s Degree or above in a quantitative field

About us: As the home to 300 Elektra, Asylum, Atlantic, East West, FFRR, Fueled by Ramen, Nonesuch, Parlophone, Rhino, Roadrunner, Sire, Warner Records, Warner Classics, and several other of the world’s premier recording labels, Warner Music Group champions emerging artists and global superstars alike. And our renowned publishing company, Warner Chappell Music, represents genre-spanning songwriters and producers through  a catalogue of more than one million copyrights worldwide. We’re the home to WMX – the next generation services division that connects artists with fans and amplifies brands in creative, immersive, and engaging ways – and Alternative Distribution Alliance (ADA) – the ground-breaking global distribution company for independent artists and labels. Together, we are Warner Music Group: Music With Vision & Voice. 
WMG is committed to inclusion and diversity in all aspects of our business. We are proud to be an equal opportunity workplace and will evaluate qualified applicants without regard to race, religious creed, color, age, sex, sexual orientation, gender, gender identity, gender expression, national origin, ancestry, marital status, medical condition as defined by state law (genetic characteristics or cancer), physical or mental disability, military service or veteran status, pregnancy, childbirth and related medical conditions, genetic information or any other characteristic protected by applicable federal, state or local law.
Copyright © 2023 Warner Music Inc.
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