Applied AI/ML Scientist Intern — Winter 2027 (Hybrid)

Faire Wholesale Inc.; San Francisco (CA); Hybrid; USD 85,000 - 121,000; Part time

Benefits offered by this job

Hybrid work model

Job summary

Faire Wholesale Inc. is seeking an Applied AI/ML Scientist, Intern for Winter 2027 in San Francisco. You will work on shipping and fulfillment models, including predicting packing, optimizing packing to reduce costs, and inferring product characteristics from catalog signals.

You will survey literature, prototype models in PyTorch and offline benchmarks, and collaborate with scientists to test on live traffic, with a focus on delivering actionable recommendations and robust experiments.

Qualifications

• Currently enrolled in or recently graduated from a Master’s or PhD program in Computer Science, Machine Learning, Statistics, Electrical Engineering, or related technical field.
• Hands-on experience building deep learning models (e.g., PyTorch) with images/text or both, including fine-tuning models or 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.

Responsibilities

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

Skills

Python

PyTorch

SQL

Statistics

Reading research papers

Canadian?

Education

Master's or PhD student

Computer Science/ML/Statistics

Tools

PyTorch

Embeddings

Job description

Faire Wholesale Inc. is seeking an Applied AI/ML Scientist, Intern for Winter 2027 in San Francisco. You will work on shipping and fulfillment models, including predicting packing, optimizing packing to reduce costs, and inferring product characteristics from catalog signals.

You will survey literature, prototype models in PyTorch and offline benchmarks, and collaborate with scientists to test on live traffic, with a focus on delivering actionable recommendations and robust experiments.