Senior Applied ML/AI Scientist — Search & Relevance Scale

Faire Wholesale Inc.; San Francisco (CA); Hybrid; USD 211,000 - 291,000; Full time

Benefits offered by this job

Equity

Comprehensive benefits

Hybrid work model

Job summary

Faire is a technology wholesale platform empowering independent retailers worldwide. We are seeking a Search ML engineer to own components from framing to production, integrating LLMs, embedding retrieval, and multi-stage ranking to deliver personalized product feeds with low latency.

The role requires deep experience in modern retrieval stacks, strong Python skills, and the ability to communicate clearly with cross-functional teams. Hybrid work and equity are offered.

Qualifications

• 3+ years building production ML systems in search, recommendations, or retrieval.
• Shipped models that served real traffic and led experiments proving value.
• Deep knowledge of retrieval stacks — dual encoders, LLM-based query understanding, learning-to-rank fundamentals.
• Excellent Python engineering skills and production readiness.
• Clear communication with scientists, engineers, and PMs.
• Strong product judgment linking customer and business needs to technical decisions.

Responsibilities

• Contribute to the next-generation Search engine, integrating LLMs, query understanding, dense vector retrieval, deep personalization, multi-stage ranking, and reinforcement learning.
• Own one or more search components end to end from framing to production code.
• Ship to live traffic and let AB tests settle what works.
• Collaborate to raise the team's bar through design reviews and post-mortems.

Skills

Production ML

Query understanding

Python programming

Communication

Product judgment

Experiment design

Tools

Python

Job description

Faire is a technology wholesale platform empowering independent retailers worldwide. We are seeking a Search ML engineer to own components from framing to production, integrating LLMs, embedding retrieval, and multi-stage ranking to deliver personalized product feeds with low latency.

The role requires deep experience in modern retrieval stacks, strong Python skills, and the ability to communicate clearly with cross-functional teams. Hybrid work and equity are offered.