Senior Applied AI/ML Scientist, Faire

• 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 execution—you move quickly from paper to prototype to production
• Strong Python skills, deep respect for system reliability and ownership, and experience operating in high-stakes environments
• Excellent communication and cross-functional influence that raise the technical bar beyond your immediate team

Desirable

• 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
• 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