Master Thesis focusing on developing machine learning models for lithium - ion cell sorting at Fraunhofer LBF. Involvement in innovative projects addressing circular economy in battery recycling.
About the role
- Machine Learning Engineer creating and maintaining ML models for intelligent automation and forecasting at Create Music Group. Collaborating with multiple teams to implement AI-driven solutions.
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
- 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
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.
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