Applied AI/ML Scientist, Intern

🏢 Faire · 📍 San Francisco, CA

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Position Summary & Responsibilities

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

Our Applied AI/ML Science team builds and maintains the models that power the marketplace. That work includes the shipping and delivery estimates retailers rely on as they shop and check out: what it will cost to ship and when it will arrive, before the order has been packed. Within Applied Science, our Shipping and Fulfillment team builds the models behind those estimates, including models that learn to understand products from images and text. Better predictions give retailers confidence in what they're buying and, in turn, help brands sell more on Faire.

What you will be doing

You'll own a focused project in one of these areas:

Predicting how an order will be packed: Before an order ships, we have to anticipate how it will be packed. You'll build models that learn from what each item is and how items combine in a cart.
Recommending better ways to pack: How an order is packed shapes what it costs to ship. You'll develop models that understand items from images and text and learn from historical packing outcomes to recommend packing that reduces shipping cost and improves efficiency.
Understanding products from a catalog: Listings often lack reliable weight, size and shape. You'll train multimodal deep learning models that infer these physical characteristics from images, text and other catalog signals.

Whichever project you take on, you will:

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🎯 Candidate Preparation Guide For Faire Applicants

Key Role Competencies & Evaluation Highlights

Key Role Focus:

Measures analytical horsepower, marketplace dynamics intuition, and extreme ownership in a hyper-growth retail tech environment.

📋 Preparation Priorities (Key Areas to Clarify):

1. Focusing purely on enterprise features while ignoring the nuanced needs of independent retailers
2. Relying on gut feel instead of rigorous metrics during case studies
3. Low tolerance for fast-paced operational pivots

✨ Recommended Role Alignment Hook:

I scale two-sided marketplaces by obsessing over unit economics and removing friction so independent retailers can compete effortlessly.

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