1d:I[34901,["/_next/static/chunks/1q_xs299ft_73.js","/_next/static/chunks/1hsg494-jz4os.js","/_next/static/chunks/1zxhbpwnpkk6f.js","/_next/static/chunks/08v7mhpebw2l_.js"],"JobTable"] 1e:T1f1b,

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:

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.

#LI-DNI

Hybrid Faire employees currently go into the office 3 days per week on Tuesdays, Thursdays, and a third flex day of their choosing (Monday, Wednesday, or Friday). Additionally, hybrid in-office roles will have the flexibility to work remotely up to 4 weeks per year. Specific Workplace and Information Technology positions may require onsite attendance 5 days per week as will be indicated in the job posting.

Why you’ll love working at Faire

Move fast: You'll own meaningful problems that serve customers around the globe with the agency to move fast and see your results clearly.
Equipped to scale: We invest in what matters, including the latest enterprise AI tools, to help you work smarter and get more out of every day.
Best in class: Our team is full of sharp, kind, and generous colleagues who care about their craft and about helping you grow in yours.
Real rewards. Competitive pay, equity, and comprehensive benefits designed to support your life inside and outside of work.
Belonging: We're intentional about building an environment where every Faire employee has equal access to opportunities, growth, and success.

Faire was founded in 2017 by a team of early product and engineering leads from Square. We’re backed by some of the top investors in retail and tech including: Y Combinator, Lightspeed Venture Partners, Forerunner Ventures, Khosla Ventures, Sequoia Capital, Founders Fund, and DST Global. We have headquarters in San Francisco and Kitchener-Waterloo, and a global employee presence across offices in Toronto, London, and New York. To learn more about Faire and our customers, you can read more on our blog.

Faire provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, genetics, sexual orientation, gender identity or gender expression.

Faire is committed to providing access, equal opportunity and reasonable accommodation for individuals with disabilities in employment, its services, programs, and activities. Accommodations are available throughout the recruitment process and applicants with a disability may request to be accommodated throughout the recruitment process. We will work with all applicants to accommodate their individual accessibility needs. To request reasonable accommodation, please fill out our Accommodation Request Form (https://bit.ly/faire-form)

Privacy

For information about the type of personal data Faire collects from applicants, as well as your choices regarding the data collected about you, please visit Faire’s Privacy Notice (https://www.faire.com/privacy)

1b:["$","div",null,{"className":"flex flex-col gap-6","children":[["$","$L1d",null,{"jobs":[{"id":"cmv21rbvx00uru901qozk082z","companyId":"cmsl1d4sf001r4s2b4wc9i8z5","sourceId":"cmsl1d4va001t4s2b9fbulh2p","externalId":"8870975002","dedupeHash":"434c9020982ee784a04485a0fe1fe6a6eb29eba9d391ab295b82bbce8d5f37f7","title":"Applied AI/ML Scientist, Intern","descriptionHtml":"$1e","applyUrl":"https://boards.greenhouse.io/faire/jobs/8870975002?gh_jid=8870975002","locationRaw":"San Francisco, CA","locationCity":"San Francisco","locationState":"CA","locationCountry":"United States","workMode":"UNKNOWN","roleType":"INTERNSHIP","isSenior":false,"season":"Winter 2027","postedAt":"$D2026-10-06T20:16:58.000Z","closesAt":null,"firstSeenAt":"$D2026-10-10T07:02:29.546Z","lastSeenAt":"$D2026-10-13T07:18:49.099Z","status":"OPEN","company":{"id":"cmsl1d4sf001r4s2b4wc9i8z5","name":"Faire","slug":"faire","logoUrl":null}}],"now":"$D2026-10-13T20:55:26.499Z","bookmarked":null,"from":"faire","caption":"Company jobs","empty":{"title":"No open roles of this kind","description":"This company has nothing open in this category right now. Try another role type, or browse the whole board.","action":"$L1f"}}],"$L20"]}]