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.
About this role
As a Senior Applied AI/ML Scientist on the Search group, you’ll help shape the technical vision, machine-learning algorithm strategy, and system design behind one of our most important growth levers: Search (think about what you do when you land on any e-commerce site). You’ll advance real-time Search and Recommendation systems that power next-generation shopping experiences.
You’ll work at the frontier 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 and brands for every user query.
This is a rare chance to influence end-to-end personalization in a high-scale, deeply multi-modal environment while collaborating closely with a talented team of scientists and engineers.
What you’ll do
• Contribute to our next-generation Search engine by integrating LLMs, query understanding, dense-vector retrieval, deep personalization embeddings, multi-stage ranking, and reinforcement learning to serve personalized product feeds with sub-100 ms latency.
• Design and productionize natural-language search and discovery systems so that intelligent agents can generate relevant and personalized collections, explain search results, and assist retailers with browsing, filtering, and evaluation.
• Lead model development and GPU-based deployment efforts, leveraging frameworks like Triton to scale inference reliably and efficiently.
• Share best practices around model development, agent-workflow evaluation, and MLOps, and help teammates level up through code reviews and technical guidance.
Qualifications
• 4+ years of experience building large-scale ML systems, including 2+ years in search, recommendation, or ads ranking.
• Hands-on experience with deep-learning libraries (e.g. PyTorch) and vector-search infrastructure (e.g. Faiss, ScaNN, Pinecone).
• A strong record of productionizing models that blend LLMs (e.g. BERT, GPT-class) with structured features to drive personalization.
• A product-focused mindset and a bias toward exec