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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead Machine Learning Engineer - **Company:** Universal Music Group. - **Location:** Santa Monica, CA, United States - **Experience:** Expert - **Salary:** $200,000 - $250,000 - **Contract:** Permanent contract - **Skills:** AI Evaluation, Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, BigQuery, Cloud Database, Code Review, Continuous Integration, Software Debugging, Decision Support Systems, Distributed Systems, Data Flow Control, Fraud Prevention and Detection, Python (Programming Language), Machine Learning, Recommender Systems, Regression Testing, Azure Machine Learning, Software Engineering, Workflow Management Systems, Feature Engineering, Large Language Models, Real-time Inference, Information Technology, Data Lineage, Machine Learning Operations, Drift Detection, Software Version Control, Data Pipelines, Automation Anywhere, Api Management - **Published:** November 6, 2026 - **Apply:** https://www.jobmonkeyjobs.com/career/28164668/Lead-Machine-Learning-Engineer-California-Santa-Monica-1018 ## About the Role * 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., The actual base salary offered depends on a variety of factors, which may include, as applicable, the qualifications of the individual applicant for the position, years of relevant experience, specific and unique skills, level of education attained, certifications or other professional licenses held, and the location in which the applicant lives and/or from which they will be performing the job. All candidates are encouraged to apply. ## Description * 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.