We are a fashion discovery platform. We have 6.2 million registered users who use our app primarily to find products visually — photographing something they like in the real world and finding shoppable equivalents from 3,400 brand partners. Visual search is the product. The accuracy and speed of our image matching and similarity ranking directly determines whether users come back. Our current visual search pipeline is built on a contrastive learning model we trained eighteen months ago. It performs well on our top 40 brand partners whose catalogue images are high-resolution, consistent, and well-structured. It performs poorly on our long-tail brand partners whose catalogue imagery is inconsistent in background, lighting, and angle. We are hiring a contract Computer Vision Engineer for a 10-month engagement to improve similarity search performance specifically on long-tail catalogue imagery, and to build the evaluation infrastructure to measure improvements systematically rather than anecdotally. The work involves both model improvement and the unglamorous but essential work of building a test set that actually measures what the users experience.