Faire is looking to drive the technical vision, ML algorithm strategy, and system design powering one of the most critical levers for customer value and company growth—Search. The goal is to advance real-time Search and Recommendation systems behind their next-generation shopping experiences.
Requirements
- 7+ years of experience building large-scale ML systems, including 3+ 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 track record of productionizing models that blend LLMs (e.g. BERT, GPT-class) with structured features to drive personalization.
- Strong Python skills, deep respect for system reliability and ownership, and experience operating in high-stakes environments.
Responsibilities
- Own 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 <100ms 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.
- Lead model development and GPU-based deployment efforts, leveraging frameworks like Triton to scale inference reliably and efficiently.
Other
- A product-focused mindset and a bias toward execution—you move quickly from paper to prototype to production.
- Excellent communication and cross-functional influence—you raise the technical bar beyond your immediate team.
- Effective January 2025, Faire employees will be expected to go into the office 2 days per week on Tuesdays and Thursdays.
- Additionally, hybrid in-office roles will have the flexibility to work remotely up to 4 weeks per year.
