Senior Applied AI/ML Scientist - Marketplace Quality
Faire · San Francisco, CA
About this role
**About Faire** Faire is a technology wholesale platform built on the belief that the future is local. Independent retailers around the globe represent a multi-hundred-billion-dollar wholesale market that has historically been fragmented and offline. At Faire, we use tech, data, and machine learning to connect this community of entrepreneurs worldwide—helping retailers discover great products from around the world to sell in their stores. **About this role** Faire leverages machine learning and data insights to help local retailers compete against giants like Amazon and big box stores. On the Marketplace Quality team, you’ll own the modeling and measurement that keeps Faire’s marketplace trustworthy for hundreds of thousands of independent brands and retailers. You’ll build systems that: - Match Faire’s catalog against messy external data at scale - Detect pricing and policy violations with calibrated confidence - Decide which violations to act on under a finite operations budget You’ll work across structured and unstructured data (listing text, product images, external web listings, transaction history) using entity resolution, information extraction, multi-modal LLMs, calibrated classification, constrained optimization, and experimentation—driving projects end-to-end from framing through production and measurement. **What you’ll do** - Own applied ML projects end-to-end: frame the problem, build and ship the model, and measure impact - Build and improve pricing-integrity models to detect over- and under-pricing violations with calibrated confidence - Solve product matching and entity resolution at scale (text + image embeddings, retrieval, multi-modal LLMs) - Extract structured attributes from unstructured listing content (descriptions, images, third-party sources) - Turn model scores into action by designing targeting/prioritization logic under a constrained human-review budget - Build human-in-the-loop systems with marketplace operations partners (audits, training labels, precision bars, feedback loops) - Design and analyze experiments for enforcement levers (downranking, badging, brand-facing remediation) and measure effects on retailer trust and GMV - Partner across product, engineering, operations, and analytics to ship models and drive business impact **Qualifications** - 3+ years of industry experience using machine learning to solve real-world problems - Experience with relevant business problems (e-commerce, marketplaces, catalog/content quality, search, or personalization) - Experience with relevant technical methods (deep learning/LLMs, computer vision, information extraction, entity resolution, ranking, experimentation, and/or 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 cross-functionally **Great to Haves** - Master’s or PhD in Computer Science, Statistics, or related STEM fields - Experience with catalog quality, product attribute extraction, computer vision for e-commerce imagery, or search/discovery for two-sided platforms - Experience building and validating LLM evaluation pipelines (prompt iteration on labeled data, human-in-the-loop workflows) **Salary Range (San Francisco)** - $211,000 to $290,500 per year - Equity and benefits also included; actual base pay depends on permissible factors (transferable skil
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