Applied Artificial Intelligence/Machine Learning Scientist, Faire

• 1-3 years of industry or research experience using machine learning to solve real-world problems, ideally including work in search, recommendations, or ads ranking
• Hands-on experience with deep-learning libraries (e.g., PyTorch) and vector-search infrastructure (e.g., Faiss, ScaNN, Pinecone)
• A strong track 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—you raise the technical bar beyond your immediate team

Desirable

• As an Applied AI/ML Scientist on the Search Group, you’ll work with world-class machine learning scientists to execute the ML algorithm strategy and system design powering one of the most critical levers for customer value and company growth—Search. You’ll collaborate on 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 and brands for any given query
• This is a rare opportunity to work on end-to-end Search/Recommendation in a high-scale, deeply multi-modal environment, where we bring together the top talents from companies including Google, Meta, Amazon, LinkedIn, Uber, Airbnb, Salesforce, Twitter, Square, Pinterest, Instacart, and Coupang
• Contribute to 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 <100 ms latency
• Design and productionize natural-language search and discovery systems, enabling intelligent agents to generate relevant and personalized collections, explain search results, and assist retailers in browsing, filtering, and evaluation
• Collaborate on model development and GPU-based deployment efforts, leveraging frameworks like Triton to scale inference reliably and efficiently