Senior Applied AI/ML Scientist (Listing Quality)

• Faire leverages the power of machine learning and data insights to revolutionize the wholesale industry, enabling local retailers to compete against giants like Amazon and big box stores. Our highly skilled team of Applied AI/ML Scientists specialize in developing algorithmic solutions for notification and recommender systems, advertising attribution, and LTV predictions. We are dedicated to building machine learning models that help our customers thrive
• As a Senior Applied AI/ML Scientist on the Listing Quality team, you will own the modeling and measurement for the content that powers Faire’s catalog: the images, titles, descriptions, and structured attributes across millions of products from hundreds of thousands of independent brands. Listing quality is one of the highest-leverage surfaces on the marketplace
• Better images and richer product information make products easier to find, easier to evaluate, and easier to buy, and they compound across search, recommendations, and the product detail page. You will work primarily with unstructured data using multi-modal deep learning and LLMs, and you will drive projects end-to-end from framing through production and measurement
• Our team already includes experienced Applied AI/ML Scientists from Uber, Airbnb, Square, Facebook, and Pinterest. Faire will soon be known as a top destination for data scientists and machine learning engineers, and you will help take us there!
• Own applied ML projects end-to-end: framing the problem, building and shipping the model, and measuring impact
• Use multi-modal deep learning and LLMs to understand listing content, extract structured product attributes, and detect quality issues at catalog scale
• Improve product imagery through hero image selection, image ordering, cropping, and enhancement, so that the best representation of a product is the one retailers see
• Build ranking and exploration approaches (e.g. bandit-style selection) that learn which content performs best for which audience
• Improve listing text: titles, descriptions, and product information coverage, and measure the downstream effect on discovery and conversion
• Build LLM-as-judge and human-in-the-loop evaluation systems, and hold them to a measurable accuracy bar before they gate production decisions
• Partner across product, engineering, design, and analytics to turn models into shipped product and business impact, and to drive brand-facing nudges that improve listings at the source
• Solve challenging problems related to a two-sided marketplace

Benefits

• Comprehensive healthcare: Including Health, Dental, Vision and Disability for all our locations.
• Time off: Paid time off, holidays and company-wide “Faire Fundays”.
• Parental leave: Generous parental and family leave, as well as fertility support benefits.
• Productivity support: Monthly stipends to help cover work from home connectivity needs.
• Annual learning grant: For personal and professional development, as well as unlimited access to training courses through LinkedIn Learning.
• Thoughtfully designed spaces: All of our offices have been designed with local in mind – from our architects to our coffee blends.
• Fitness and well-being benefits: Including monthly credit towards your wellness-related programmes.
• Mental health benefits: Including free access to Modern Health therapists and resources.
• Charitable matching: Faire will match up to £250 of your charity donations, every year.
• Career planning: Whether you want to grow as a leader, hone your craft or explore a new discipline, our career framework allows space for you to explore.- 3+ years of industry experience using machine learning to solve real-world problems
• Experience with relevant technical methods (deep learning and LLMs, computer vision, information extraction, entity resolution, ranking, and/or experimentation and causal inference)
• The ability to drive a project end-to-end and lead model development with limited supervision
• An excitement and willingness to learn new tools and techniques
• Strong communication skills and the ability to work in a highly cross-functional team
• Strong programming skills
• Experience with relevant business problems (e-commerce, marketplaces, catalog and content quality, search, or personalization)
• Previous experience with catalog quality, product attribute extraction, computer vision for e-commerce imagery, or search and discovery for a two-sided platform
• Highly recommended: Master’s or PhD in Computer Science, Statistics, or related STEM fields
• Experience building and validating LLM evaluation pipelines, including prompt iteration against labeled data and human-in-the-loop workflows