Guillermo Mosse

Guillermo Mosse is a Machine Learning Scientist III on Wayfair's Product & Content Intelligence team, focused on production ML and GenAI systems for product-data quality and cost-efficient inference, with an emphasis on faster, more rigorous R&D.

Wayfair Tech Blog

How We Used AI as a Lab Assistant to Run 110 Experiments in Three Days and Cut Projected Tag-Validation Cost by 94%

Every Wayfair product is described by a large set of structured tags: material, shape, room, style, color, storage features, assembly details and 47,000 more. Those tags drive search, filtering, recommendations and merchandising. When a tag is wrong or missing, the customer experience degrades quickly. Products become harder to find, filters become less trustworthy and downstream systems make worse decisions.

4 Min Read

Wayfair Tech Blog

How We Validate Product Dimensions at Scale With Multimodal GenAI

Imagine using an e-commerce site like Wayfair to find the perfect sofa for your living room, only to discover when it arrives that the six-foot length in the description actually measures closer to seven feet. Now you are debating if it's fine that it overlaps the carpet (your partner is shaking their head) or if you should arrange a return and start your sofa search all over. Situations like this highlight the impact on the customer of inaccurate product dimensions on an e-commerce site. They may be frustrated, lose a little trust in e-commerce purchases and maybe even put that sofa back on a truck to the warehouse.

6 Min Read

Wayfair Tech Blog

Wayfair’s New Approach to Aspect Based Sentiment Analysis Helps Customers Easily Find “Long Tail” Products

Over thirty million customers from around the world shop on Wayfair to look for furniture that helps them create their unique sense of home. Wayfair serves a variety of webpages catered to niche customer sub-segments and help customers discover new products – these include pages with advice for people buying “twin futons,” or “nursery chairs with removable cushions.”

5 Min Read