Description
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.
Our Applied AI/ML Science team builds and maintains the models that power the marketplace. You will 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
Requirements:
- 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: $75 USD per hour.