Senior Applied AI/ML Scientist - Listing Quality

About the role

Faire is a technology wholesale platform connecting independent retailers worldwide with products from brands around the globe. Its Applied AI/ML Scientists build algorithmic solutions for notification and recommender systems, advertising attribution and LTV predictions. As a Senior Applied AI/ML Scientist on the Listing Quality team, you will own the modeling and measurement of the content powering the catalog: images, titles, descriptions and structured attributes across millions of products. You will work primarily with unstructured data using multi-modal deep learning and LLMs, driving projects end-to-end from framing through production and measurement.

Key responsibilities

• Own applied ML projects end-to-end: frame the problem, build and ship the model, and measure 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, ordering, cropping and enhancement.
• Build ranking and exploration approaches (e.g. bandit-style selection) that learn which content performs best for each 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, validated to a measurable accuracy bar before they gate production decisions.
• Partner with 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.

Required profile

• 3+ years of industry experience applying machine learning to real-world problems.
• Experience with e-commerce, marketplaces, catalog and content quality, search or personalization.
• Ability to drive a project end-to-end and lead model development with limited supervision.
• Strong communication skills and ability to work in a highly cross-functional team.
• Willingness to learn new tools and techniques.

Required skills

• Deep learning and LLMs, computer vision, information extraction, entity resolution, ranking and/or experimentation and causal inference.
• Strong programming skills.
• Nice to have: a Master's or PhD in Computer Science, Statistics or a related STEM field; prior catalog quality, product attribute extraction or e-commerce computer vision experience; experience building and validating LLM evaluation pipelines.

What we offer

• Pay range of $211,000 to $290,500 per year in San Francisco, plus equity and benefits.
• Hybrid work: 3 days in the office (Tuesdays, Thursdays and a flex day), with remote work up to 4 weeks per year.
• Paid time off, recharge days, paid parental leave up to 16 weeks, comprehensive health coverage and fertility benefits.
• Mental health support, professional development stipends and an employer-sponsored retirement plan.