Senior Applied AI/ML Scientist - Listing Quality

About Faire

Faire is a technology wholesale platform connecting independent retailers with brands through technology, data, and machine learning.

About the Role

As a Senior Applied AI/ML Scientist on the Listing Quality team, you will own modeling and measurement for images, titles, descriptions, and structured attributes across millions of products. You will work primarily with unstructured data using multimodal deep learning and large language models, driving projects from problem framing through production and measurement. Your work will improve product discovery, evaluation, and conversion across search, recommendations, and product detail pages.

Responsibilities

• Own applied machine learning projects end-to-end: frame problems, build and ship models, and measure impact.
• Use multimodal 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.
• Build ranking and exploration approaches, including bandit-style selection, to learn which content performs best for different audiences.
• Improve titles, descriptions, and product information coverage, measuring downstream effects on discovery and conversion.
• Build LLM-as-judge and human-in-the-loop evaluation systems, validating measurable accuracy before using them to gate production decisions.
• Partner with product, engineering, design, and analytics to ship models, deliver business impact, and develop brand-facing nudges that improve listings at the source.
• Solve challenging problems in a two-sided marketplace.

Qualifications

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

Preferred Qualifications

• Master's degree or PhD in Computer Science, Statistics, or related STEM fields is highly recommended.
• 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.

Compensation and Benefits

• San Francisco annual base salary range: $211,000–$290,500.
• Eligible for equity and comprehensive benefits.
• Actual base pay depends on transferable skills, work experience, market demands, and primary work location. The range may change.

Work Arrangement

Hybrid employees attend the office three days per week: Tuesdays, Thursdays, and a third day chosen from Monday, Wednesday, or Friday. Hybrid in-office roles may work remotely up to four weeks per year.

Application Information

Reasonable accommodations are available throughout recruitment. Applicants may request them through the Accommodation Request Form.
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