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
As a Staff Applied AI/ML Scientist on the Search Group, you’ll drive the
technical vision, ML algorithm strategy, and system design powering one of the
most critical levers for customer value and company growth—Search (think about
what you do when you land on any e-commerce site). You’ll lead the advancement
of real-time Search and Recommendation systems behind our next-generation
shopping experiences.
You’ll operate at the forefront of algorithms—combining large language models,
natural language processing, query understanding, deep learning,
transformer-based sequential modeling, graph neural networks, and structured
behavioral data to return hyper-relevant, personalized products/brands for any
given query from the users.
This is a rare opportunity to own end-to-end personalization in a high-scale,
deeply multi-modal environment—while mentoring a team of talented scientists and
engineers.
What you’ll do
- Own the next-generation Search engine, integrating LLMs, query understanding,
dense vector retrieval, deep personalization embeddings, multi-stage ranking,
and reinforcement learning to serve personalized product feeds with
