Applied AI / ML Scientist, Faire

• 2+ years of industry experience using machine learning to solve real-world problems, ideally in Ads, Marketplace Optimization, Pricing, Auctions, or a related large-scale production domain
• Demonstrated ownership of the full ML lifecycle: from scoping and design through training, deployment, A/B testing, and iteration
• Strong programming skills
• Experience with relational databases and SQL
• The ability to contribute to team strategy and to lead model development without supervision
• Strong communication skills and the ability to work with others in a closely collaborative team environment

Desirable

• The Ads Data team is building the next generation of advertising products for the wholesale industry
• As a key member of this team, you’ll shape the future of our ads marketplace and auction system at Faire—spanning auction design, bidding strategy, pacing, pricing, and budget optimization
• Our in-house stack gives us unique end-to-end ownership, and our first-party conversion data allows us to train highly effective models and optimize for real, business-critical outcomes on behalf of our brands
• This is a rare opportunity to be an early contributor to a fast-growing team in an incredibly strategic area of the business
• Ads is a major company priority and you'll have a massive impact in shaping the platform that connects independent retailers and brands
• You’ll work closely with engineers, product managers, and designers to launch and iterate on systems that are not just technically sophisticated, but also directly drive improvements to the bottom line for both brands and Faire
• Own the end-to-end development of ML models across the ads bidding stack—from problem framing, solution design, modeling, and implementation to deployment, experimentation, and impact measurement
• Build best-in-class models and algorithms for auction optimization, bid shading and bid multipliers, budget allocation, pacing, and advertiser ROI/ROAS optimization—drawing from marketplace optimization and experimentation best practices
• Define and improve bidding and pacing strategies that balance advertiser performance, marketplace health, and platform economics (e.g., spend smoothing, delivery guarantees, seasonal demand, and inventory constraints)
• As an early member of the Ads Data team, help define its roadmap and technical culture, leveraging deep product intuition to shape what ads at Faire *should* be—not just how they’re built
• Work in a fast-paced, collaborative environment with team members who’ve shipped ML at top tech companies (e.g. Uber, Airbnb, Meta, Amazon, Pinterest)