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
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

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.

Machine Learning Engineer

Key skills