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 turn dreams into stardom and audiences into fans. We are guided by three core values that underpin everything we do across all our diverse businesses: • Curiosity: We do our best work when we’re immersing ourselves in culture and breaking through barriers. Curiosity is the driving force behind creativity and ingenuity. It fuels innovation, and innovation is the key to our future. • Collaboration: Making music and bringing it to the world is all about the power of originality amplified by teamwork. A great idea, like a great song, travels globally. We ignite passions and build connections across our diverse community of artists, songwriters, partners, and fans. • Commitment: We pursue excellence for our team and our talent. Everything in music starts with a leap into the unknown, and we’re committed to keeping the faith, acting with integrity, and delivering on our promises. 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. WMG is home to a wide range of artists, musicians, and songwriters that fuel our success. That is why we are committed to creating a work environment that actively values, appreciates, and respects everyone. We encourage applications from people with a wide variety of backgrounds and experiences. Consider a career at WMG and get the best of both worlds – an innovative global music company that retains the creative spirit of a nimble independent. 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 build robust, performant machine learning infrastructure and industry leading music forecasting models. These engineers collaborate closely with data scientists, data engineers, product managers, application developers, and business operators. We're looking for an extremely strong machine learning manager to make the vision of using data-driven insights to grow WMG's artists and songwriters a reality.
What You'll Do:
On day one you will manage 3 MLEs directly and you will grow the team quickly to 6-10 ML engineers who are responsible for setting a new bar in music data analysis
Make technical contributions - model training, ML infra improvements, etc.
Evangelize and demonstrate to business partners how ML based forecasts and recommendations can improve their ability to grow the business
Review, set technical direction, contribute to technical decisions in modeling and measurement, etc.
Learn and grow as a professional through close collaboration with your team members and engineering leaders, and by being part of a continuous improvement and learning culture
Tech Stack - Databricks ML platform with homegrown python-based feature store. Underlying data through both Snowflake and Databricks
Requirements:
You have a Bachelor's degree in Computer Science, Mathematics, Physics, or other related field
You have at least 10 years experience as a MLE
You have at least 3 years of management experience (experience managing teams of 10-15 preferred)
Strong ML knowledge and great SWE skills
You are highly technical and analytical with experience building systems (ML infrastructure, data pipelines, or services)
Driven by solving business problems, understand how and when to use ML, and how to supplement ML with heuristics or rules to build systems that work
Should know how to talk to non technical audiences about how ML based systems can solve business problems
Responsibilities:
Develop a strong understanding of the business problems we are trying to solve
Together with Data Scientists, Data Engineers, Software Engineers and product leadership, identify and define the ML systems we want to build - and then design, implement, and launch those systems
Train, evaluate, improve and launch music forecast models
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 opportunities, project objectives and deliverables
