Senior Applied AI/ML Scientist, Faire

• 3+ years of industry experience using machine learning to solve real-world problems
• Experience with relevant business problems (e.g. e-commerce)
• Experience with relevant technical methods (e.g. LLM fine tuning, deep learning, or human-in-the-loop machine learning)
• Strong programming skills
• An excitement and willingness to learn new tools and techniques
• The ability to design and implement ML solutions without supervision
• Strong communication skills and the ability to work in a highly cross-functional team

Desirable

• Faire leverages the power of machine learning and data insights to revolutionize the wholesale industry, enabling local retailers to compete against giants like Amazon and big box stores
• At Faire, the Data Science team is responsible for creating and maintaining a diverse range of algorithms and models that power our marketplace. We are dedicated to building machine learning models that help our customers thrive
• As a member of the Brand Data Science team working on Listing Quality, you will be responsible for improving the quality of product listings to help retailers find and evaluate products on Faire
• You will use ML and AI to tackle critical challenges, such as enhancing image and text quality, extracting structured product attributes, and accurately identifying duplicates and product variants
• You will leverage deep learning, multi-modal LLMs, and human-in-the-loop training to create high performance solutions
• This space has been evolving rapidly with advancements in AI and you will be at the forefront of applying the latest technology to drive real-world impact
• You will independently design and implement solutions and work with the cross-functional Listing Quality pod, including product, design, engineering, analytics, and operations, to solve problems end-to-end
• Drive data science vision, strategy, and execution on Listing Quality, using ML and AI solutions to improve the quality of Faire’s product listings
• Use deep learning, LLM fine tuning, and human-in-the-loop training to automatically detect and address issues with high accuracy
• Act as a lead on the cross-functional Listing Quality pod, thinking end-to-end about brand and retailer experiences