Principal Machine Learning Scientist

Universal Music Group•Santa Monica, CA

19h•$275,000 - $350,000

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 Principal Machine Learning Scientist is a senior individual-contributor role combining hands-on technical work with technical leadership across cross-functional teams. The role calls for deep technical judgment, product sense, scientific and engineering rigor, and the ability to make progress in a complex, federated organization. The position leads the design and delivery of AI-enabled workflows and machine learning solutions that improve decisions across UMG. Work may span consumption and fan intelligence, catalog understanding, financial forecasting, rights, and ecosystem integrity. The scientist works with business and technical teams to frame problems, select suitable methods, and deliver solutions that achieve measurable adoption and business impact. This includes developing models, evaluating existing approaches, creating AI evaluation frameworks, and helping teams move prototypes into reliable production capabilities. The role leads initiatives from problem definition and data assessment through modeling, deployment, evaluation, monitoring, and sustained adoption. It builds and improves machine learning and statistical systems, advances generative and multimodal AI workflows, and develops decision systems that help stakeholders understand evidence and choose actions. It also establishes reusable technical practices, communicates tradeoffs to audiences ranging from practitioners to executives, influences cross-functional planning, and mentors practitioners. Success in the first 12 months includes leading high-value initiatives through production or a well-supported decision not to build; establishing trusted evaluation and monitoring; improving an existing model or workflow; helping teams make evidence-based decisions; raising technical standards; and building durable partnerships across central teams, labels, and business functions.

Requirements

Advanced training in computer science, statistics, mathematics, economics, operations research, or another quantitative field, or equivalent applied research and industry experience.
Extensive experience developing and deploying machine learning or statistical systems, with sustained ownership of ambiguous, high-impact problems.
Deep production experience in at least two relevant areas, such as recommender systems, personalization, search and ranking, causal inference, time-series forecasting, optimization, or anomaly detection.
Strong experience designing, evaluating, and deploying modern AI systems, including LLM-based workflows, retrieval, grounding, tool use, structured generation, safety controls, or human review.
A strong record of moving models from research and experimentation into reliable, maintainable, and cost-efficient production environments.
Expert Python skills and fluency with modern scientific and ML tooling, such as PyTorch, TensorFlow, JAX, scikit-learn, or equivalent frameworks.
Experience with cloud-based data and ML platforms, such as GCP, AWS, or cloud-agnostic systems.
Strong understanding of data quality, feature design, experimentation, rigorous evaluation, and model monitoring.
A record of technical leadership, mentorship, and cross-functional collaboration without relying on formal authority.
Excellent written and verbal communication, including the ability to turn complex statistical or architectural tradeoffs into actionable guidance for executives and business leaders.

Nice To Haves

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 applications for artist discovery, catalog understanding, fan marketing, rights management, fraud detection, or trust and safety.
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.
Published research, open-source contributions, patents, or active collaboration with academic researchers and applied research labs.

Responsibilities

Translate ambiguous business opportunities into clearly defined machine learning, statistical, forecasting, optimization, generative AI, or non-AI problems.
Determine whether advanced modeling adds value or a simpler approach is more appropriate.
Lead initiatives from problem definition and data assessment through modeling, deployment, evaluation, monitoring, and sustained business adoption.
Build and improve models for fan intelligence, personalization, catalog understanding, forecasting, portfolio decisions, fraud, or content integrity.
Move generative and multimodal AI capabilities from prototypes into dependable, cost-effective workflows embedded in UMG products and operations.
Build decision systems that identify meaningful changes, explain supporting evidence, and help stakeholders determine appropriate action.
Define evaluation frameworks covering quality, latency, cost, robustness, privacy, provenance, copyright risk, and model safety.
Partner with ML engineers, data engineers, product managers, designers, domain experts, and label stakeholders to create trusted, useful systems.
Establish reusable practices for data and feature access, experimentation, model serving, monitoring, governance, lineage, and documentation.
Communicate technical tradeoffs clearly to practitioners, executives, and business leaders, adjusting the level of detail to the audience.
Influence cross-functional planning, connect capabilities across teams, and help prioritize work based on value, feasibility, risk, and learning potential.
Mentor practitioners and raise technical standards through reusable methods, documentation, and reviews.
Build durable partnerships across central teams, labels, and business functions.
Improve the quality, reliability, cost efficiency, or adoption of existing models or AI-enabled workflows.

Benefits

Comprehensive medical, dental, and vision coverage.
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 (PTO) 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.