Opportunity at
FaireApplied AI/ML Scientist, Intern
Faire, a wholesale technology platform for independent retailers, offers a paid Winter 2027 internship on its Applied AI and ML Science team. The team develops the…
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About This Role
Faire, a wholesale technology platform for independent retailers, offers a paid Winter 2027 internship on its Applied AI and ML Science team. The team develops the models behind marketplace estimates such as shipping cost and delivery timing that retailers see before an order is packed. Inside that group, the Shipping and Fulfillment team creates models that interpret products from images and text. Stronger predictions give retailers more confidence and help…
Job Description
Faire, a wholesale technology platform for independent retailers, offers a paid Winter 2027 internship on its Applied AI and ML Science team. The team develops the models behind marketplace estimates such as shipping cost and delivery timing that retailers see before an order is packed. Inside that group, the Shipping and Fulfillment team creates models that interpret products from images and text. Stronger predictions give retailers more confidence and help brands sell more.
The intern is responsible for a narrow project in one of three areas: anticipating how a cart will be packed before dispatch; suggesting packing approaches that cut shipping costs and raise efficiency, drawing on historical packing outcomes; or training multimodal deep learning models that estimate weight, size and shape using photos, product text and other catalog cues when listings lack reliable physical data.
Regardless of topic, the intern reviews literature and existing methods, prototypes and trains models offline against current baselines, converts the most promising method into functioning code, partners with the applied science and ML engineering teams to try the approach on live traffic, and presents results and recommendations. The internship lasts 12 to 14 weeks from January 2027, with flexible starts for qualified candidates and possible extensions. The schedule is hybrid, with three days in the office.
Responsibilities
- Own a focused project in packing prediction, packing recommendations or catalog-based product understanding
- Review the research and existing approaches to find promising ideas
- Prototype and train models offline, comparing them against current methods
- Convert the most promising method into functioning code
- Collaborate with Applied Scientists and MLEs to try the approach on live traffic
- Share results and proposals with the group in a readout
Requirements
- Current graduate student or recent alumnus of a master's or PhD track in CS, ML, statistics, EE or nearby fields
- Practical deep learning experience (for example PyTorch) on images, text or a mix of both, including tuning pretrained networks or using embeddings
- Know your way around boosting-tree models and similar tabular ML approaches
- Strong Python and SQL
- Able to read research papers and convert promising ideas into code
- Strong footing in stats and evaluation methods, including fair baseline comparisons and uncertainty estimates
- Comfortable working with messy, incomplete real-world data
- Excited by open-ended problems and by picking up new tools and methods
Core Skills
Frequently Asked Questions
Answers are based only on the employer’s listing; where it doesn’t say, neither do we.
How long is the internship?
12 to 14 weeks, starting in January 2027. Flexible start dates are available for qualified candidates, and extensions may be offered.
What is the pay?
$75 USD per hour in San Francisco. The posting says the actual rate depends on factors such as skills, experience, market conditions and location.
Who can apply?
Students in a graduate program, or recent graduates of one, in CS, ML, statistics, EE or related fields.
Does Faire use AI in hiring?
Yes. The posting says Faire uses AI to screen and choose applicants for this position.
Sample Interview Questions
AI-generated questions tailored to this specific role — a preview of the full practice set.