Machine Learning​/AI Engineer

At Warner Music Group, we are 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, You Tube, 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. 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 and AI 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 who have applied their rigor and deep understanding of Machine Learning to build robust AI applications. Our ideal candidate will have strong MLE and Data Science skills along with a passion for building efficient, robust software across the entire AI application stack, from model development and operations of traditional ML models to the development and deployment of production applications leveraging GenAI foundation models.

Responsibilities

• Develop a strong understanding of the business problems we are trying to solve.
• Partner with tech leadership and data scientists to identify business problems amenable to machine learning solutions. Own the entire AI application lifecycle from model definition and data preparation to deployment, monitoring, and maintenance.
• Design, implement, and launch systems that enable scalable AI applications, including robust data pipelines, feature stores, and high-throughput model serving APIs.
• Ensure that we are able to run modeling experiments and launches with maximal efficiency, quality, reliability, and repeatability in a large‑scale environment with > 2 TB of incoming data per day and a total corpus in excess of 20 PB.
• Establish and maintain MLOps practices to automate and streamline the entire AI development lifecycle, including experiment tracking, model registries, and automated retraining and deployment.
• Mentor more junior MLEs and Data Scientists.
• Work closely with cross‑functional partners to define project objectives and deliverables.

Requirements

• 3+ years of full‑time hands‑on experience building scaled ML systems, training large ML models, or equivalent experience.
• 1+ year of AI engineering experience.
• Bachelor’s Degree or above in a quantitative field.
• Excellent coding and system design skills.
• Strong practical ML knowledge, working knowledge of ML theory, and a deep understanding of the AI application stack and lifecycle in the context of foundation models, from evaluation to deployment.
• Demonstrated experience making an…