<div>About Faire Faire is an online wholesale marketplace built on the belief that the future is local — independent retailers around the globe are doing more revenue than Walmart and Amazon combined, but individually, they are small compared to these massive entities. At Faire, were 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 that small businesses everywhere can compete with these big box and e-commerce giants. By supporting the growth of independent businesses, Faire is driving positive economic impact in local communities, globally. 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. </div>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 <ul> <li>Drive data science vision, strategy, and execution on Listing Quality, using ML and AI solutions to improve the quality of Faire’s product listings.</li> <li>Use deep learning, LLM fine tuning, and human-in-the-loop training to automatically detect and address issues with high accuracy.</li> <li>Act as a lead on the cross-functional Listing Quality pod, thinking end-to-end about brand and retailer experiences.</li> </ul> Qualifications <ul> <li>3+ years of industry experience using machine learning to solve real-world problems</li> <li>Experience with relevant business problems (e.g. e-commerce)</li> <li>Experience with relevant technical methods (e.g. LLM fine tuning, deep learning, or human-in-the-loop machine learning)</li> <li>Strong programming skills</li> <li>An excitement and willingness to learn new tools and techniques</li> <li>The ability to design and implement ML solutions without supervision</li> <li>Strong communication skills and the ability to work in a highly cross-functional team </li> </ul> Great to Haves: <ul> <li>Master’s or PhD in Computer Science, Statistics, or related STEM fields is highly recommended</li> <li>Previous experience in listing quality for e-commerce</li> <li>Previous experience in supervised fine tuning of multi-modal LLMs</li> <li>Experience deploying and optimizing LLM inference systems at scale (10B+ tokens), with focus on cost efficiency and product impact</li> </ul> Salary Range Canada: the pay range for this role is $168,000 to $231,000 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.<div>Hybrid Faire employees currently go into the office 2 days per week on Tuesdays and Thursdays. Effective starting in January 2026, employees will be expected to go into the office on 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.  Applications for this position will be accepted for a minimum of 30 days from the posting date. Why you’ll love working at Faire <ul> <li>We are entrepreneurs: Faire is being built for entrepreneurs, by entrepreneurs. We believe entrepreneurship is a calling and our mission is to empower entrepreneurs to chase their dreams. Every member of our team is taking part in the founding process.</li> <li>We are using technology and data to level the playing field: We are leveraging the power of product innovation and machine learning to connect brands and boutiques from all over the world, building a growing community of more than 350,000 small business owners.</li> <li>We build products our customers love: Everything we do is ultimately in the service of helping our customers grow their business because our goal is to grow the pie - not steal a piece from it. Running a small business is hard work, but using Faire makes it easy.</li> <li>We are curious and resourceful: Inquisitive by default, we explore every possibility, test every assumption, and develop creative solutions to the challenges at hand. We lead with curiosity and data in our decision making, and reason from a first principles mentality.</li> </ul> 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<a href="https://blog.faire.com/"> blog</a>. 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 <a href="https://docs.google.com/forms/d/e/1FAIpQLSczpI9me7nYKhunSC6AlagHKv75LHqfi-Qi2TP_3dpin0Uc9Q/viewform">Accommodation Request Form</a> (<a href="https://bit.ly/faire-form)">https://bit.ly/faire-form)</a> 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 <a href="https://www.faire.com/privacy" target="_blank">Privacy Notice (https://www.faire.com/privacy)</a></div>

Faire

Senior Data Scientist / Machine Learning Engineer - Listing Quality

Faire

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