• Own the full ML lifecycle including feature pipelines, training workflows, model deployment, inference services, monitoring, and retraining • Design and build reliable data and feature pipelines • Build and optimize machine learning models including regression, classification, ranking, and recommender systems • Implement and manage batch scoring pipelines and online inference services • Partner with data scientists to operationalize models • Collaborate with software engineers for integration into production services • Establish observability for ML systems • Design systems for rapid experimentation and safe rollout of new models • Document architecture, establish best practices, and mentor teammates

Job Summary We are building CreateOS — a next-generation operating system for modern record labels — and AI is at the center of it. As a Machine Learning Engineer, you will be a core builder on the team responsible for designing, training, deploying, and maintaining the ML systems that power intelligent automation, forecasting, anomaly detection, and agentic workflows across the platform. This is a hands-on engineering role for someone who is equally comfortable prototyping a new model and shipping it to production. You will work directly with the VP of AI & ML Engineering and partner closely with Data Engineering, Analytics, and Product to move AI from experimentation into reliable, scalable production systems embedded in real user workflows. Your immediate impact will be on three of CMG's highest-priority AI initiatives: M&A catalog valuation forecasting, AI-driven A&R discovery, and marketing automation agents — each directly tied to revenue growth and competitive differentiation. Responsibilities Applied Machine Learning - Build and maintain ML models for forecasting, anomaly detection, classification, ranking, and optimization across music industry use cases (catalog valuation, royalty reasoning, A&R intelligence, marketing performance, etc.) - Partner with Analytics & BI to identify, engineer, and validate features that drive meaningful predictive power - Own the full ML lifecycle — from problem framing and data exploration through training, evaluation, deployment, and monitoring MLOps & Production Engineering - Deploy and monitor models in production using modern MLOps tooling - Instrument models for performance tracking, drift detection, and continuous improvement - Implement CI/CD, automated testing, model versioning, and observability for all ML systems - Collaborate with Data Engineering to ensure data quality, feature delivery, and pipeline reliability Agentic AI & LLM Systems (Stretch) - Develop and maintain modular AI agents that automate multi-step workflows across CreateOS (contracts, accounting, distribution, metadata) - Build and iterate on RAG pipelines, retrieval architectures, and semantic search systems grounded in structured business data - Implement guardrails, evaluation frameworks, and safe action boundaries for agentic systems Collaboration & Communication - Translate business problems from non-technical stakeholders into well-scoped ML solutions - Document model design decisions, evaluation results, and known limitations clearly - Contribute to a culture of engineering rigor and responsible AI development - Other duties as assigned Qualifications - 4+ years of software engineering experience in a production environment, with exposure to ML or data science work (academic, professional, or project-based); OR 2+ years of experience specifically as an ML Engineer or Applied Data Scientist - Strong proficiency in Python and ML frameworks (PyTorch, scikit-learn, XGBoost, or similar) - Hands-on experience building, deploying, and monitoring models in cloud environments — GCP strongly preferred (AWS or Azure acceptable); familiarity with services such as Vertex AI, BigQuery, Cloud Functions, and Cloud Run is a strong plus - Solid understanding of modern ML techniques — supervised/unsupervised learning, time series forecasting, embeddings, ranking — and their mathematical foundations - Experience with LLMs and prompt engineering, including building RAG systems or LLM-powered features - Comfortable working with structured and unstructured data at scale - Strong communication skills with the ability to explain complex model behavior to non-technical audiences Preferred Qualifications - MS in Machine Learning, Data Science, Business Analytics, Computer Science, Statistics, or a related quantitative field; OR 4+ years of experience as an ML Engineer or Applied Data Scientist in lieu of an advanced degree - Experience at a startup or high-growth technology company where you owned features end-to-end - Familiarity with agentic frameworks (LangChain, LangGraph, AutoGen, or similar) - Background in music, media, entertainment, or rights/royalties data - Experience with ontology design, knowledge graphs, or semantic data modeling - Contributions to open-source ML projects or published research - Familiarity with AI evaluation practices — hallucination detection, bias auditing, model explainability Pay Scale - $160,000 - $200,000/USD per year - The final compensation within this range will be determined based on the candidate’s experience, skills, and overall fit for the role. Fair Chance Policy In accordance with the Los Angeles County Fair Chance Ordinance, we will consider employment for qualified applicants with criminal histories. We evaluate candidates based on their qualifications and the nature of the offense in relation to the job for which they are applying.

• Responsable del desarrollo exitoso, automatización y gobernanza de soluciones de IA/ML para los clientes internos • Colaborar con científicos de datos, expertos del dominio y propietarios de productos para construir y desplegar productos de IA de principio a fin para la empresa • Requerirá un conocimiento práctico de Cloud, DevOps/MLOps, Machine Learning, Deep Learning y desarrollo de aplicaciones • Involucrarse en la formulación de requisitos comerciales clave • Producción de código listo para la producción • Aprovechar los recursos de GPU y CPU según corresponda y entender los requisitos de capacidad para cargas de trabajo de ML • Arquitectar, automatizar y orquestar canalizaciones de Dev-Ops/ML-Ops en la nube • Diseñar e implementar canalizaciones de ingeniería de datos (ETL) • Entender problemas comerciales y colaborar dentro del equipo para asegurar escalabilidad, continuidad del negocio y tiempos de respuesta apropiados

• Design and build scalable AI infra and services to power agentic AI applications • Develop advanced reasoning and agentic capabilities that enable AI agents to operate autonomously and adaptively in dynamic, real-world environments • Implement monitoring, evaluation, and optimization processes to ensure reliability and responsiveness in production • Stay at the forefront of agentic AI innovation, bringing emerging techniques into practical application to shape product direction • Collaborate closely with applied scientists, engineers, and product teams to translate experimental prototypes into robust production systems • Contribute to best practices in distributed ML systems, scalable architecture, and responsible AI deployment