Machine Learning Engineer

Create Music Group•Los Angeles, CA

30d•$160,000 - $200,000

About The Position

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.

Requirements

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

Nice To Haves

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

Responsibilities

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
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
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
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