Senior Applied ML/AI Scientist - Search

About Faire

Faire is a technology wholesale platform connecting independent retailers with products and brands through technology, data, and machine learning.

About the Role

As a Senior Applied AI/ML Scientist on the Search team, you will help shape the technical vision, machine-learning algorithm strategy, and system design for Search. You will advance real-time search and recommendation systems that deliver personalized shopping experiences.

The work combines query understanding, deep learning, transformer-based sequential modeling, graph neural networks, and structured behavioral data to surface relevant products and brands. You will collaborate closely with scientists and engineers on end-to-end personalized discovery in a high-scale, multimodal environment.

Responsibilities

• Build next-generation search ranking algorithms using deep learning and machine learning to personalize retailer discovery.
• Use large language models to extract text and visual signals that improve understanding of users and their intents.
• Partner with teams across Faire to experiment with and improve models for search ranking and other applications.
• Design and productionize natural-language search and discovery systems that enable intelligent agents to generate personalized collections, explain search results, and assist with browsing, filtering, and evaluation.
• Share best practices for deep learning model development, agent-workflow evaluation, and MLOps.
• Support teammates through code reviews and technical guidance.

Qualifications

• At least 5 years of industry experience building large-scale ML models with business impact and shipping ML solutions to production, including at least 3 years in search, recommendation, or ads ranking.
• A master's degree or PhD in Computer Science, Statistics, or a related STEM field.
• Strong programming skills in Python, Java, or an equivalent language, with hands-on experience using deep-learning libraries such as PyTorch and big data technologies such as Spark.
• Deep understanding of ML best practices, including training and serving, imbalanced data, A/B testing, feature engineering, and feature/model selection.
• Deep understanding of algorithms such as user modeling, deep learning, and reinforcement learning, applied to search, recommendation, and advertising.
• A product-focused mindset and bias toward execution, moving quickly from research papers to prototypes and production.
• Excellent written and verbal communication and strong cross-functional influence.

Preferred Qualifications

• Contributions to open-source ML libraries or peer-reviewed ML/AI publications.
• Industry experience developing and productizing LLM-based search applications and systems.
• Industry experience building search and recommendation systems for e-commerce or two-sided marketplaces.
• Experience using AI tools such as Cursor, Claude Code, or Codex for development and daily productivity.
• Familiarity with Kotlin.

Location and Work Arrangement

• Locations: New York City, NY, or San Francisco, CA.
• Hybrid employees attend the office three days per week: Tuesdays, Thursdays, and one chosen day on Monday, Wednesday, or Friday.
• Hybrid in-office roles offer flexibility to work remotely for up to four weeks per year.

Compensation and Benefits

• Annual US base salary range: $211,000–$290,500.
• Eligible for equity and comprehensive benefits.
• Actual base pay depends on transferable skills, experience, market demands, and primary work location. The range may change.

Application Accessibility

Reasonable accommodations are available throughout recruitment. Applicants may request accommodations through the Accommodation Request Form.