ML Engineer​/Data Scientist, Applied AI

Core Values

• 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.

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.

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.

This role is the technical engine of our AI transformation. You will be responsible for bringing our most impactful AI models out of the lab and scaling them into reliable, high‑performance production systems.

Reporting to the VP Data Solutions & Innovation within the Business Intelligence organization, you will lead the technical effort in exploring, validating, and accelerating the next generation of AI use cases. Your mission is focused on rapid scientific discovery and robust engineering: you will design and execute advanced modeling experiments to unlock new business value, and you will ensure that the most successful prototypes are engineered into scalable, high‑performance production systems.

You will operate with an innovator’s mindset, tackling complex, unstructured music and market data, using techniques such as Deep Learning and Generative AI. Your core objective is to maximize the rate of successful innovation and reliably deploy verified solutions, ensuring our entire BI ecosystem is propelled toward predictive and augmented intelligence.

• Rapid Modeling & Experimentation:
  Design, develop, and benchmark state‑of‑the‑art machine learning models (forecasting, segmentation, recommendation, NLP, etc.) with a strong emphasis on quick iteration and scientific validation of new concepts.
• Generative AI & Exploration:
  Lead hands‑on technical exploration into advanced techniques, including LLMs, RAG architectures, and Generative AI applications to create new forms of automated analysis and augmented intelligence products.
• Production Engineering & MLOps:
  Translate validated prototypes into robust, production‑ready specifications, and lead the implementation of MLOps best practices (CI/CD, monitoring, serving) required for the reliable deployment of models.
• Complex Data & Feature Engineering:
  Deeply explore complex, multi‑modal data (e.g., high‑dimensional data, text, time series) defining the necessary features and data pipelines to support highly accurate experimental models for strategic analysis.
• Cross‑Functional
  
  Collaboration:
  
  Work closely with the Product Manager, Data Scientists, and business stakeholders to ensure technical solutions maximize tangible business impact and adhere to ethical AI standards.
• Technology Scouting:
  Drive innovation through hands‑on exploration of new AI technologies, including LLMs, GenAI, and vector databases, and evaluate their practical application to our music and operational data.
• Knowledge Transfer:
  Contribute to AI adoption and technical literacy across the company through clear documentation, workshops, and knowledge sharing with both technical and non‑technical teams.

• Education:
  
  Bachelor’s degree required in Applied Mathematics, Computer Science, Software Engineering, or a highly technical quantitative discipline. A Master’s degree (MS) or higher is strongly preferred.
• Experience:
  
  2+ years of professional experience as a Machine Learning Engineer, Applied ML Scientist,…