AI/ML Engineer — Classification + Matching

We're building an AI system that reads content and automatically determines:

Whether it contains a paid endorsement (yes / no)
What type of endorsement it is, based on our classification spec
Which brand it refers to — matched against our existing brand database, with a suggested new brand name when there's no match

We already have a labeled set of examples across the different endorsement types that you can use for training and evaluation.

What you'll do
Build a classification pipeline that flags paid vs. non-paid endorsements and categorizes endorsement type per our specification
Build brand matching (entity resolution) against our database using fuzzy and/or semantic matching, with a "suggest a new brand" fallback when nothing matches
Use our labeled examples to train and/or tune the system, and set up an evaluation framework so we can measure and improve performance per task
Process large volumes of entries efficiently and cost-effectively
Document the approach and hand off a maintainable system

What you should have
Strong experience building NLP / text-classification systems
Hands-on work with modern LLMs (e.g., Claude, GPT) — prompt engineering, structured outputs, and/or fine-tuning
Experience with entity resolution / record linkage — fuzzy or semantic matching, embeddings, vector search
A rigorous, evaluation-driven approach — you measure performance properly rather than by feel
Comfort with our stack: Python, SQL, working with APIs and a database