About the job
About Faire
Faire is a pioneering online wholesale marketplace that champions the idea that the future is local. Independent retailers across the globe generate more revenue than industry titans like Walmart and Amazon combined, yet they remain individually small compared to these giants. At Faire, we harness technology, data analytics, and machine learning to connect this vibrant community of entrepreneurs worldwide. Think of your favorite local boutique; we empower them to discover outstanding products from around the globe to feature in their stores. With the right insights and tools, we aim to level the playing field, enabling small businesses to compete effectively against big box and e-commerce behemoths.
By nurturing the growth of independent businesses, Faire contributes positively to local economies on a global scale. We seek intelligent, resourceful, and passionate individuals to join us in advancing the shop local movement. If you are a believer in community, come be a part of ours.
About this Role:
At Faire, we leverage cutting-edge machine learning and data-driven insights to transform the wholesale industry, allowing local retailers to stand up to giants such as Amazon and big box stores. Our talented team of data scientists and machine learning engineers is dedicated to crafting algorithmic solutions for search, personalization, recommender systems, and ranking. Our mission is to empower local retail businesses with the essential tools for success.
We are on the lookout for outstanding Master's and PhD candidates with a focus on recommender systems, personalization, or applied machine learning.
This role is perfect for candidates who possess:
- A demonstrated passion for recommender systems and personalization
- Experience with contemporary ML techniques in ranking and representation learning
- For PhD candidates: a history of publications or submissions to top-tier conferences (e.g., KDD, RecSys, ICML, NeurIPS, WWW, SIGIR)
- For Master’s candidates: impactful research projects, internships, or open-source contributions in relevant fields
In this position, you will tackle fundamental personalization challenges that influence millions of daily recommendations, collaborating closely with ML engineers to translate research concepts into production-ready solutions.
What You’ll Work On
- Design and implement advanced recommender systems for product ranking and discovery
- Develop user and item representation learning methodologies...

