Netflix developed MediaFM, an in-house multimodal content embedding model to understand its vast catalog. This model aims to create a machine-readable understanding of content by integrating audio, video, and text. MediaFM uses a Transformer-based encoder to generate shot-level embeddings, capturing temporal relationships. The model's inputs are shot-level embeddings from video, audio, and timed text, concatenated and processed. A Masked Shot Modeling objective trains the model to predict original embeddings. Evaluation involves task-specific linear layers, proving MediaFM's superior performance compared to baselines. The model significantly improves in tasks demanding nuanced understanding, such as ad relevancy. Contextualization plays a critical role with an added modality. MediaFM benefits applications across Netflix, including ad relevancy and clip popularity prediction. The team is now exploring how to use pre-trained multimodal LLMs for future model development. The blog post concludes with gratitude for contributors and a glimpse into future work.