About this role

Faire is an online wholesale marketplace that connects independent brands with local retailers. We use machine learning and data insights to help those small businesses compete against giants like Amazon and big box stores.

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.

We're looking for people who thrive on tricky problems: who dig into rich data, come up with ideas that work, and take the best of them all the way to production.

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:

• Survey the literature and existing approaches to identify promising ideas
• Prototype and train models offline, benchmarking against our current methods
• Build out the strongest approach into a working implementation
• Work with Applied Scientists and ML Engineers to test it against live traffic
• Present findings and recommendations to the team

What it takes

• Currently enrolled in or recently graduated from a Master's or PhD program in Computer Science, Machine Learning, Statistics, Electrical Engineering, or a related technical field
• Hands-on experience building deep learning models (e.g., PyTorch), ideally with images, text, or both, including fine-tuning pre-trained models or working with embeddings
• Familiarity with gradient-boosted trees and other tabular ML methods
• Strong Python and SQL
• Ability to read research papers and turn promising ideas into working code
• Solid grounding in statistics and model evaluation, including benchmarking against strong baselines and estimating uncertainty
• Comfort working with noisy or incomplete real-world data
• A genuine enthusiasm for tackling ambiguous problems and learning new tools and techniques

Internship Details

This paid Winter 2027 internship runs for 12 to 14 weeks, beginning in January 2027, with flexible start dates available for qualified candidates. Extensions may be offered based on project needs and mutual agreement.

Pay rate

San Francisco: the pay rate for this role is $75 USD per hour.

Actual hourly pay will be determined based on permissible factors such as transferable skills, work experience, market demands, and primary work location. The pay range provided is subject to change and may be modified in the future.

Faire uses Artificial Intelligence (AI) to screen and select applicants for this position.

This job posting is for an existing vacancy.

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