Senior Applied AI/ML Scientist - Listing Quality

About Faire Faire is a technology wholesale platform built on the belief that the future is local. Independent retailers around the globe collectively represent a multi-hundred-billion-dollar wholesale market that has historically been fragmented and offline. At Faire, we're using the power of tech, data, and machine learning to connect this thriving community of entrepreneurs across the globe. Picture your favorite boutique in town — we help them discover the best products from around the world to sell in their stores. With the right tools and insights, we believe that we can level the playing field so businesses can grow and local communities can thrive. We’re looking for smart, resourceful and passionate people to join us as we power the shop local movement. If you believe in community, come join ours. About this role 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. What you’ll do 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. Qualifications 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 Great to Haves: Master’s or PhD in Computer Science, Statistics, or related STEM fields is highly recommended Previous experience in listing quality for e-commerce Previous experience in supervised fine tuning of multi-modal LLMs Experience deploying and optimizing LLM inference systems at scale (10B+ tokens), with focus on cost efficiency and product impact Salary Range San Francisco: the pay range for this role is $211,000 to $290,500 per year. This role will also be eligible for equity and benefits. Actual base pay will be determined based on permissible factors such as transferable skills, work experience, market demands, and primary work location. The base pay range provided is subject to change and may be modified in the future. Hybrid Faire employees currently go into the office 3 days per week on Tuesdays, Thursdays, and a third flex day of their choosing (Monday, Wednesday, or Friday). Additionally, hybrid in-office roles will have the flexibility to work remotely up to 4 weeks per year. Specific Workplace and Information Technology positions may require onsite attendance 5 days per week as will be indicated in the job posting. Why you’ll love working at Faire Move fast: You'll own meaningful problems that serve customers around the globe with the agency to move fast and see your results clearly. Equipped to scale: We invest in what matters, including the latest enterprise AI tools, to help you work smarter and get more out of every day. Best in class: Our team is full of sharp, kind, and generous colleagues who care about their craft and about helping you grow in yours. Real rewards. Competitive pay, equity, and comprehensive benefits designed to support your life inside and outside of work. Belonging: We're intentional about building an environment where every Faire employee has equal access to opportunities, growth, and success. Faire was founded in 2017 by a team of early product and engineering leads from Square. We’re backed by some of the top investors in retail and tech including: Y Combinator, Lightspeed Venture Partners, Forerunner Ventures, Khosla Ventures, Sequoia Capital, Founders Fund, and DST Global. We have headquarters in San Francisco and Kitchener-Waterloo, and a global employee presence across offices in Toronto, London, and New York. To learn more about Faire and our customers, you can read more on our blog . Faire provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, genetics, sexual orientation, gender identity or gender expression. Faire is committed to providing access, equal opportunity and reasonable accommodation for individuals with disabilities in employment, its services, programs, and activities. Accommodations are available throughout the recruitment process and applicants with a disability may request to be accommodated throughout the recruitment process. We will work with all applicants to accommodate their individual accessibility needs. To request reasonable accommodation, please fill out our Accommodation Request Form ( https://bit.ly/faire-form) Privacy For information about the type of personal data Faire collects from applicants, as well as your choices regarding the data collected about you, please visit Faire’s Privacy Notice (https://www.faire.com/privacy)

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