faire · Kitchener-Waterloo, ON; Toronto, ON
Skills: React, Next.js, TypeScript, JavaScript, HTML/CSS, Java, Kotlin, SQL, AWS, Accessibility
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. Why Listing Quality at Faire? Our mission in the Brand pillar is to make it easy for Brands to create high-quality product listings on Faire, keep them up to date, and get them in front of retailers. Having access to well-curated catalogs of products lets Main Street retailers compete with big-box stores, and we’re responsible for the first step in that journey. As a Senior Product Engineer on the Listing Quality team, you’ll make every product on Faire look and read its best. We use computer vision and LLMs to improve hero images, crops and product descriptions across millions of listings, and we prove the impact through online experiments. About this role: You’ll partner closely with Brand Data Science. They own model quality. You own getting model outputs into the product reliably, at catalog scale, and measurably. When it’s faster to test an idea yourself, you do: tune the prompt, build the eval, and run the experiment. This is an ideal role for engineers who want to work where machine learning meets production systems, and who care as much about whether retailers click as whether the job succeeded. You like turning a data scientist’s prototype into something t…
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