Senior Applied AI/ML Scientist - Marketplace Quality

About Faire

Faire is a technology wholesale platform that connects independent retailers with brands and products worldwide, using technology, data, and machine learning to support local businesses.

About the Role

As a Senior Applied AI/ML Scientist on the Marketplace Quality team, you will own modeling and measurement that help keep Faire's marketplace trustworthy. You will match Faire's catalog against messy external data at scale, detect pricing and policy violations with calibrated confidence, and prioritize enforcement within a finite operations budget.

The work spans listing text, product images, external web listings, and transaction history, using entity resolution, information extraction, multimodal LLMs, calibrated classification, constrained optimization, and experimentation. You will drive projects from framing through production and measurement alongside product, engineering, analytics, and marketplace operations.

Responsibilities

• Own applied ML projects end-to-end, from problem framing and model development to deployment and impact measurement.
• Build pricing-integrity models that compare Faire listings with external pricing signals and detect over- and under-pricing violations with calibrated confidence.
• Link Faire's catalog to external listings using text and image embeddings, retrieval, and multimodal LLMs; build match-quality and gating models for reliable downstream detection.
• Extract structured attributes from descriptions, images, and third-party sources for detection and enrichment.
• Design targeting and prioritization logic that balances expected marketplace impact, false-positive costs, and constrained human-review capacity.
• Build human-in-the-loop systems with marketplace operations, including audits, training labels, precision thresholds, and feedback from review outcomes.
• Design and analyze experiments for enforcement mechanisms such as downranking, badging, and brand-facing remediation; measure effects on retailer trust and marketplace GMV.
• Partner across functions to translate models into shipped products and business impact.
• Solve challenging problems associated with a two-sided marketplace.

Qualifications

• At least 3 years of industry experience applying machine learning to 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, including 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

• A 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 is subject to change.
• Access to enterprise AI tools and opportunities for professional growth.

Work Arrangement

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

Application Information

Applicants who need reasonable accommodation during recruitment may submit Faire's Accommodation Request Form.

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