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Senior Applied Ai Ml Scientist Search

💼 Full-time💰 $168,000–$231,000🗓 2026-07-25

Core

Design and productionize next-generation Search and Recommendation systems using LLMs, NLP, and deep learning to deliver hyper-relevant, personalized product feeds for e-commerce users.

Role type

Senior Applied AI/ML Scientist (Search & Recommendation)

Builds

Real-time search engines, dense-vector retrieval systems, multi-stage ranking models, and intelligent agent workflows for product discovery.

Domain

E-commerce, Search, Recommendation Systems, Large Language Models

Deliverable

production ML models

Required skills

Large-scale ML system design, LLM integration, NLP, query understanding, deep learning, transformer-based sequential modeling, graph neural networks, reinforcement learning, Python, Triton, MLOps

Preferred skills

Open-source ML contributions, peer-reviewed publications

Technologies

PyTorch, Faiss, ScaNN, Pinecone, BERT, GPT-class models, Triton

Responsibilities

Integrate LLMs and vector retrieval for sub-100 ms latency personalization; design natural-language search systems for intelligent agents; lead GPU-based model deployment and scaling; mentor teammates on model development and MLOps.

Seniority

Senior, hands-on IC

Rewrite
## About the 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 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. ## Great to Haves - Contributions to open-source ML libraries or peer-reviewed publications in ML/AI. - Master's or PhD in Computer Science, Statistics, or a related STEM field. ## What we offer - Equity and benefits. ## About the company Faire uses Artificial Intelligence (AI) to screen and select applicants for this position.
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