Senior Data Scientist​/Machine Learning Engineer - Listing Quality

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, 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 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.

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.

• 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

• 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

San Francisco: the pay range for this role is $192,000 to $264,000 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.

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…