About Faire

Faire is an online wholesale marketplace built on the belief that the future is local — independent retailers around the globe are doing more revenue than Walmart and Amazon combined, but individually, they are small compared to these massive entities. 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 that small businesses everywhere can compete with these big box and e-commerce giants.

By supporting the growth of independent businesses, Faire is driving positive economic impact in local communities, globally. 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:

Faire leverages the power of machine learning and data insights to revolutionize the wholesale industry, enabling local retailers to compete against giants like Amazon and big box stores. Our highly skilled team of data scientists and machine learning engineers specialize in developing algorithmic solutions for search, personalization, recommender systems, and ranking. Our ultimate goal is to empower local retail businesses with the tools they need to succeed.

We are looking for exceptional Master’s and PhD candidates specializing in recommender systems, personalization, or applied machine learning.

This role is ideal for candidates who have:

Demonstrated strong interest in recommender systems / personalization

Experience with modern ML approaches to ranking and representation learning

For PhD candidates: a track record of publications or submissions to top-tier venues (e.g., KDD, RecSys, ICML, NeurIPS, WWW, SIGIR)

For Master’s candidates: high-impact research projects, internships, or open-source work in relevant areas

You will work on core personalization problems that directly affect millions of recommendations per day, partnering closely with ML engineers to bring research ideas into production.

What You’ll Work On

Design and deploy state-of-the-art recommender systems for ranking and discovery

Develop user and item representations using embeddings, sequence models, or graph-based methods

Build systems leveraging real-time and streaming signals for dynamic personalization

Apply exploration–exploitation techniques (e.g., contextual bandits, reinforcement learning)

Improve diversification, novelty, and long-term user engagement

Run large-scale A/B experiments to evaluate model performance in production

Contribute to the end-to-end ML lifecycle: problem formulation → modeling → offline evaluation → online experimentation

Why This Role is Unique

Research → Production impact: Your work will directly ship and affect marketplace outcomes

Rich, real-world data: Sparse, noisy, and high-dimensional data at scale

Challenging objectives: Multi-sided marketplace optimization (retailers, brands, platform)

Strong ML culture: Close collaboration with experienced ML engineers and applied scientists

Opportunity to publish and build on prior research, where applicable

Basic Qualifications

Currently pursuing or recently completed a Master’s or PhD in Computer Science, Machine Learning, Statistics, or a related quantitative field

Proficiency in Python and familiarity with the modern ML stack (e.g., PyTorch, TensorFlow, Pandas, SQL)

A solid theoretical foundation in machine learning and statistics

Preferred Qualifications

Publications or submissions in top-tier venues such as KDD, RecSys, ICML, NeurIPS, WWW, SIGIR

Experience with:

Recommender systems (collaborative filtering, deep recommenders, ranking)

Representation learning / embeddings

Sequential models (RNNs, Transformers for user behavior)

Bandits / reinforcement learning

Large-scale retrieval and ranking systems

Familiarity with offline evaluation metrics (NDCG, MAP, recall) and online experimentation

Experience working with large-scale or production datasets

Internship Details

Duration: 12–14 weeks (flexible start dates)

Location: San Francisco

Paid internship with potential for extension or return offer

What We Look For

We value candidates who:

Think deeply about modeling trade-offs and real-world constraints

Care about both research rigor and practical impact

Can independently drive ambiguous problems from idea to measurable outcome

Are excited about applying cutting-edge ML to help small businesses thrive

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.

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

We are entrepreneurs: Faire is being built for entrepreneurs, by entrepreneurs. We believe entrepreneurship is a calling and our mission is to empower entrepreneurs to chase their dreams. Every member of our team is taking part in the founding process.

We are using technology and data to level the playing field: We are leveraging the power of product innovation and machine learning to connect brands and boutiques from all over the world, building a growing community of more than 350,000 small business owners.

We build products our customers love: Everything we do is ultimately in the service of helping our customers grow their business because our goal is to grow the pie - not steal a piece from it. Running a small business is hard work, but using Faire makes it easy.

We are curious and resourceful: Inquisitive by default, we explore every possibility, test every assumption, and develop creative solutions to the challenges at hand. We lead with curiosity and data in our decision making, and reason from a first principles mentality.

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)

Data Science Intern (Personalization & Recommender Systems)

Job Description