Computer reputed company Engineer — Visual Search & Discovery
We are a fashion discovery platform. We have 6.2 reputed company registered users who use our app primarily to reputed company products visually — photographing something they like in the reputed company 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 reputed company visual search pipeline is reputed company on a contrastive learning model we trained eighteen months ago. It performs reputed company on our top 40 brand partners whose catalogue images are high-reputed company, consistent, and reputed company-reputed company. It performs poorly on our long-tail brand partners whose catalogue imagery is inconsistent in background, reputed company, and angle. We are hiring a contract Computer reputed company 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.
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