Lead Machine Learning Engineer

We are UMG, the Universal Music Group. We are the world’s leading music company. In everything we do, we are committed to artistry, innovation and entrepreneurship. We own and operate a broad array of businesses engaged in recorded music, music publishing, merchandising, and audiovisual content in more than 60 countries. We identify and develop recording artists and songwriters, and we produce, distribute and promote the most critically acclaimed and commercially successful music to delight and entertain fans around the world. How we LEAD: Universal Music Group is building an Applied AI and Machine Learning function to help our teams discover and develop artists, connect with fans, make stronger commercial decisions, and protect the integrity of the music ecosystem. We are looking for a Lead Machine Learning Engineer to build, ship, and operate reliable machine learning and AI systems that create durable value across UMG. This is a senior individual-contributor role for an engineer who combines strong software and systems expertise with practical knowledge of the AI and ML lifecycle. You will turn models, data products, and AI workflows into maintainable production capabilities across revenue forecasting, artist, catalog intelligence and talent scouting, release marketing, and rights and ecosystem integrity. The role focuses on reproducible development, rigorous evaluation, safe deployment, observability, cost efficiency, and continuous improvement. How you’ll CREATE: Core MLOps & Production Infrastructure • Lead initiatives from problem framing and data assessment through deployment, evaluation, monitoring, and sustained adoption (versus reach a well-evidenced decision not to build). • Build and operate reliable ML and LLM-powered workflows, including retrieval and grounding, structured outputs, tool and API integrations, orchestration, batch or real-time inference, CI/CD, and safe rollback. • Establish AI evaluation and observability using representative and adversarial test sets, regression tests, human review where appropriate, traces, drift detection, latency, quality, safety, usage, and cost metrics. Data & Feature Engineering • Build reliable data and feature pipelines from streaming consumption data, release metadata, social campaign data, catalog information, and commercial finance systems; monitor completeness, freshness, anomalies, and data lineage. • Develop and operate models across personalization, recommender systems, search and ranking, time-series forecasting, anomaly detection, and decision support—choosing a simpler reliable approach whenever is appropriate. Business Stakeholders • Move generative and multimodal AI capabilities from prototypes into dependable, cost-effective workflows that help people make decisions and take action. • Partner with ML scientists, data engineers, product managers, designers, domain experts, A&R, Marketing, Finance, and labels; contribute to architecture, code reviews, mentoring, and pragmatic build-versus-buy decisions. What success looks like in your first 12 months • Ship production ML or AI capabilities that improve a meaningful UMG decision or workflow. • Establish automated evaluation and regression coverage for AI workflows, with trusted measures for quality, safety, latency, cost, and business usefulness. • Improve the reliability, observability, performance, scalability, or cost-efficiency of existing workflows. • Move a prototype or manual process into a dependable workflow used for a real decision, such as allocating marketing spend, prioritizing an A&R opportunity, reviewing a forecast, or investigating an integrity alert. • Raise the technical bar through documentation, reviews, and mentorship. • Effective collaboration and communication with business stakeholders, labels, central teams, and other non-technical partners. Bring your VIBE: • A bachelor’s or advanced degree in computer science, engineering, mathematics, or a related field, or equivalent practical experience. • Typically 5+ years of experience in software, machine learning, data, or platform engineering with ownership of production systems and ambiguous, high-impact problems. • Experience engineering modern AI systems, including LLM-based workflows, retrieval, grounding, structured generation, tool use, safety controls, and human review, with clear evaluation and monitoring practices. • Strong Python and software engineering skills, including testing, code review, version control, debugging, maintainable design, and experience with a second production language or equivalent systems experience. • Experience with cloud-based data and ML platforms. UMG workloads use GCP and BigQuery; experience with services such as Vertex AI, Dataflow, or Cloud Composer is valuable, while equivalent AWS or cloud-agnostic experience is also relevant. • Experience building and operating data pipelines, distributed systems, APIs, services, workflow orchestration, model serving, batch or online inference, and refresh or retraining processes. • Ability to collaborate across technical and business disciplines, communicate tradeoffs clearly, take ownership without formal authority, and choose a simpler reliable solution when it is better than a more complex model. Especially valuable • Experience in music, media, entertainment, streaming marketplaces, advertising, or another industry where culture, consumer behavior, and business conditions change rapidly. • Experience working with complex non-tabular data, such as audio, text, images, or video. • Experience building decision-support products and automated workflows rather than standalone models or static dashboards. • Experience establishing model governance, data provenance, copyright safety protocols, or human-in-the-loop review systems. • Experience building systems for artist discovery, catalog understanding, fan marketing, rights management, fraud detection, or trust and safety. Perks Playlist: Join an entrepreneu