Research Engineer, Content Understanding

A research engineer in content understanding typically needs strong machine learning and data skills, plus the ability to turn messy text and documents into clean, usable signals. A strong candidate brings hands on experience building and evaluating models, comfort with large datasets, and care about measuring quality. At an applied AI lab building search for models and agents, that work likely feeds directly into how well content is found and understood.

This kind of role sits close to where AI is changing fastest, since models now read and summarize content  human part stays in framing the problem, judging what good understanding looks like, and checking results that look right but are not. Careful evaluation and judgment matter more as generation gets cheaper.

The work offers hands on practice with retrieval, embeddings, and evaluation of model output on real content. Building small experiments that compare approaches on a clear task is a practical way to prepare. Experience with search or document pipelines is a useful starting point.

• Text and document pipelines
• Retrieval and embeddings
• Python and ML tooling

This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.

Exa is an applied AI lab that builds a search engine and API for AI models and agents, per the company.