ML Engineer / Data Scientist, Applied AI
Job Description:
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.
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.
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.
Mission
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.
Key responsibilities
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.
Skills & Experience
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, or similar role, with a clear focus on productionizing models and advanced AI techniques.
Technical Depth: Strong expertise in Python development and established skills in deploying and managing the full lifecycle of complex ML/DL models. Experience with advanced analysis of unstructured or multi-modal data (e.g., high-dimensional feature vectors, dense embeddings) is highly valued.
MLOps Mindset: Proven track record of transforming R&D proofs-of-concept into robust, scalable, and monitored production-grade ML solutions.
Engineering Rigor: A background in software engineering best practices (clean code, testing, Git) is essential.
Communication: Exceptional ability to communicate complex concepts and model limitations clearly and effectively to product and non-technical stakeholders.
Domain Affinity: High curiosity and enthusiasm for music, entertainment, or culture is a strong plus.
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, religion or belief, age, sex, sexual orientation, gender, gender identity or gender reassignment, marital or civil partnership status, disability, pregnancy, childbirth or any other characteristic protected by law.
