Senior Applied AI/ML Scientist - Listing Quality

About this role

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!

What you’ll do

• 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.

Qualifications

• 3+ years of industry experience using machine learning to solve real-world problems.
• Experience with relevant business problems (e-commerce, marketplaces, catalog and content quality, search, or personalization).
• Experience with relevant technical methods (deep learning and LLMs, computer vision, information extraction, entity resolution, ranking, and/or experimentation and causal inference).
• Strong programming skills.
• An excitement and willingness to learn new tools and techniques.
• The ability to drive a project end-to-end and lead model development with limited supervision.
• Strong communication skills and the ability to work in a highly cross-functional team.

Great to Haves:

• Highly recommended: Master's or PhD in Computer Science, Statistics, or related STEM fields.
• Previous experience with catalog quality, product attribute extraction, computer vision for e-commerce imagery, or search and discovery for a two-sided platform.
• Experience building and validating LLM evaluation pipelines, including prompt iteration against labeled data and human-in-the-loop workflows.

Salary Range

San Francisco: the pay range for this role is $211,000 to $290,500 per year.

This role will also be eligible for equity and benefits. Actual base pay will be determined based on permissible factors such as transferable skills, work experience, market demands, and primary work location. The base pay range provided is subject to change and may be modified in the future.