Marketing AI/ML Engineer
morningstar
Job Description
- Build and scale AI-driven systems that support marketing measurement, experimentation, and decision-making
- Develop automation and agent-based workflows that reduce manual analysis and operational overhead
- Ensure outputs are interpretable, reliable, and aligned to business context
- Contribute to a modern marketing intelligence ecosystem combining ML, GenAI, and analytics engineering
This role is not about owning a single model or tool. It is about helping Marketing move faster and smarter by embedding AI into how work actually gets done.
Responsibilities
- Design, develop, and deploy machine learning and generative AI solutions for marketing use cases
- Build and maintain scalable data and model pipelines across the ML lifecycle (data prep, modeling, evaluation, deployment, monitoring)
- Develop GenAI capabilities including prompt workflows, embeddings, and retrieval-augmented generation (RAG) patterns
- Contribute to AI agents and automation workflows that streamline marketing analysis and operations
- Partner with Marketing and Analytics teams to translate business needs into technical solutions
- Perform data preparation, feature engineering, and validation across marketing and enterprise data sources
- Integrate AI outputs into dashboards, tools, and downstream workflows
- Document systems, models, and outputs to ensure transparency and usability
Requirements
- Bachelor’s degree required; Master’s preferred in a quantitative field
- 1–3 years of experience in ML, data science, analytics engineering, or software engineering
- Strong foundation in machine learning (regression, classification, clustering, evaluation)
- Proficiency in Python and SQL for data and model development
- Experience with standard ML/data libraries (Pandas, NumPy, Scikit-learn)
- Familiarity with GenAI concepts (prompting, embeddings, vector search, evaluation)
- Exposure to modern data platforms (Snowflake, Databricks, BigQuery) and version control (Git)
- Ability to work cross-functionally and communicate technical concepts clearly