Lead Machine Learning Engineer

Universal Music Group•Santa Monica, CA

2d•$200,000 - $250,000

About The Position

Universal Music Group is building an Applied AI and Machine Learning function to help its teams discover and develop artists, connect with fans, make stronger commercial decisions, and protect the integrity of the music ecosystem. The Lead Machine Learning Engineer is a senior individual-contributor role responsible for building, shipping, and operating reliable machine learning and AI systems that create durable value across UMG. The role turns models, data products, and AI workflows into maintainable production capabilities across revenue forecasting, artist and catalog intelligence, talent scouting, release marketing, and rights and ecosystem integrity. It emphasizes reproducible development, rigorous evaluation, safe deployment, observability, cost efficiency, and continuous improvement. The engineer works with technical and business partners to move AI capabilities from prototypes into dependable workflows used for decisions and action.

Requirements

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, and maintainable design; experience with a second production language or equivalent systems experience.
Experience with cloud-based data and ML platforms. UMG workloads use GCP and BigQuery; 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.

Nice To Haves

Experience with GCP services such as Vertex AI, Dataflow, or Cloud Composer.
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.

Responsibilities

Lead initiatives from problem framing and data assessment through deployment, evaluation, monitoring, and sustained adoption, or 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, and latency, quality, safety, usage, and cost metrics.
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 for personalization, recommender systems, search and ranking, time-series forecasting, anomaly detection, and decision support, choosing a simpler reliable approach when appropriate.
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.
Ship production ML or AI capabilities that improve meaningful UMG decisions or workflows.
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.
Raise the technical bar through documentation, reviews, and mentorship, and collaborate effectively with business stakeholders, labels, central teams, and other non-technical partners.

Benefits

Comprehensive medical, dental, and vision coverage, including 100% coverage for out-patient in-network mental health services.
Fertility coverage for eligible medical plan participants.
Wellbeing reimbursements for fitness classes, spa treatments, meal services, travel, and more, up to $720 per year.
Student Loan Repayment Assistance and Tuition Reimbursement.
401(k) with 100% immediate vesting on the first 5% of contributions, plus an additional UMG contribution.
Flexible Paid Time Off for exempt employees.
Three weeks of PTO for non-exempt employees.
Two weeks of paid Winter Break.
Ten company holidays, including Juneteenth and Wellbeing Day.
Summer Fridays between Memorial Day and Labor Day.
Generous paid parental leave for every type of parent.