Applied AI/ML 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