Content Engineer, Meta Superintelligence Labs

Content Engineering is a horizontal function in the Muse Product Post Training org within Meta Superintelligence Labs (MSL) that shapes AI product experiences by aligning models to be helpful in new product experience through productizing prompt engineering, frontier evaluations, and quality frameworks. Sitting at the intersection of user experience and large-language model behavior, content engineers partner closely with research science, engineering, product and design teams to build and ship AI experiences from lab to production, across modalities and surfaces.

Great Content Engineers have the aesthetic taste to notice what makes a model output great, the technical prompting expertise to align models to consistently provide great responses, experience at building auto-gradeable frontier evals for new capabilities, and the experience delivering high scale datasets via human annotators and synthetic data. This role goes beyond shaping how the model communicates — it owns the quality bar. This person defines what "great" looks like across product capabilities, builds the evaluation infrastructure (human and automated) to measure it, and runs the feedback loops that turn qualitative insight into model improvement. They operate as a cross-functional bridge between research science, engineering, product, and policy, translating user-facing quality problems into structured priorities and actionable fixes. They serve as a point of contact for internal stakeholders across AI on priority product and model initiatives, and aim to build experiences that entertain, inform, and delight.