About this role
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.
Data Science Internship — Multiple Teams
Faire leverages machine learning and data insights to transform the wholesale industry, giving independent retailers the tools to compete with large-scale e-commerce platforms and big-box stores. Our Data Science team builds and maintains the algorithmic systems — spanning search, personalization, recommendation, and ranking — that power our marketplace and help our customers thrive.
We are hiring Data Science interns across several teams and are looking for intellectually curious, self-directed problem solvers eager to work end-to-end on high-impact challenges, from data exploration to production-ready solutions.
Our internships are paid, 12–14 weeks in duration, with flexible start dates. Extensions are considered based on project scope and mutual interest.
Open Teams
Search & Recommendation
Design and deploy state-of-the-art recommender systems that power ranking and discovery across the marketplace Develop rich user and item representations through embeddings, sequence models, and graph-based methods Build real-time and streaming data pipelines that enable dynamic, context-aware personalization at scale Apply exploration–exploitation strategies — including contextual bandits and reinforcement learning — to optimize recommendations under uncertainty Advance recommendation quality through improvements to diversification, novelty, and long-term user engagement Own the full ML lifecycle: from problem formulation and modeling through offline evaluation and online experimentation
Fulfillment
Develop ML models that predict product demand for brands leveraging Faire's fulfillment services, informing replenishment decisions, reducing stockouts, and improving inventory reliability across the platform Apply machine learning and optimization techniques to enhance the efficiency of Faire's fulfillment operations, including inventory placement, order packing logic, and operational workflow improvements Collaborate with the Discovery team and internal stakeholders to optimize surfaces that promote fulfillment products to retailers, driving discoverability and supporting the growth of Faire's fulfillment offering
Risk Management
Build and refine models and heuristics across core risk domains — including underwriting, identity verification, returns, markdowns, and disputes & misuse — to reduce financial losses and unlock GMV growth Partner cross-functionally to develop scalable, data-driven frameworks that balance risk exposure with business opportunity
What You'll Do
Design, develop, and A/B test cutting-edge machine learning algorithms and analytical solutions, with guidance from senior technical leads Communicate project objectives, methodologies, and results clearly to both immediate teammates and broader cross-functional stakeholders Navigate the complexity of a two-sided marketplace, identifying and addressing the unique challenges that arise at the intersection of retailer and brand needs
What We're Looking For
All candidates must be currently enrolled or recently graduated Master's or PhD students in Computer Science, Operations Research, Statistics, Econometrics, or a related technical discipline. Beyond that, we're looking for team-specific experience:
Search & Recommendation Systems
Publications or submissions to top-tier venues such as KDD, RecSys, ICML, NeurIPS, WWW, or SIGIR Experience with recommender systems (collaborative filtering, deep recommenders, ranking), representation learning and embeddings, sequential models (RNNs, Transformers for user behavior modeling), bandit and reinforcement learning methods, and 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
Fulfillment
Experience with Python; familiarity with Java, Kotlin, or C++ is a plus Production-level experience building and deploying ML systems Working knowledge of statistical methods, including experimentation and causal inference Experience with SQL or other query languages preferred Genuine enthusiasm for tackling ambiguous problems and learning new tools and techniques
Risk Management
Solid ML fundamentals with hands-on experience productionizing models using frameworks such as scikit-learn, XGBoost, or deep learning libraries Experience with Python; familiarity with Java, Kotlin, or C++ is a plus Knowledge of statistical techniques including experimentation and causal inference Experience with SQL or other database querying languages preferred
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)