Faire · Posted 2w ago
Senior Applied AI/ML Scientist (Marketplace Quality)
Job relayed by Stapply Data, its source: you apply there.
- Location
- 🇺🇸 San Francisco, United States
- Contract
- Full-time
- Role
- Data science
- Source
- Stapply Data
Stack
- LLM
The posting
Join Faire, a company revolutionizing the wholesale industry through machine learning and data insights. As a Senior Applied AI/ML Scientist on the Marketplace Quality team, you will own the modeling and measurement that keeps Faire's marketplace trustworthy for independent brands and retailers. You will work across structured and unstructured data, drive projects end-to-end, and partner closely with product, engineering, and marketplace operations.
- Posséder des projets d'apprentissage automatique de bout en bout : définir le problème, construire et expédier le modèle, et mesurer l'impact sur le marché. - Construire et améliorer les modèles d'intégrité des prix qui comparent les annonces de Faire aux signaux de prix externes et détectent les violations de sur- et sous-prix. - Résoudre le problème de la correspondance des produits et de la résolution d'entités à grande échelle : lier le catalogue de Faire aux annonces externes en utilisant des embeddings de texte et d'image.
- 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 - Strong programming skills - Experience with relevant technical methods (deep learning and LLMs, computer vision, information extraction, entity resolution, ranking, and/or experimentation and causal inference) - Experience with relevant business problems (e-commerce, marketplaces, catalog and content quality, search, or personalization) - 3+ years of industry experience using machine learning to solve real-world problems - 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